October 30, 2017

CEC Sessions

CEC-1  Special Session on Many-Objective Optimization
CEC-2  Special Session on Memetic Computing
CEC-3  Special Session on Evolutionary Robotics
CEC-4  Special Session on New Directions in Evolutionary Machine Learning
CEC-5  Special Session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction
CEC-6  Special Session on Evolutionary Computing Application in Hardware
CEC-7  Special Session on Data-Driven Evolutionary Optimization of Computationally Expensive Problems
CEC-8  Special Session on Transfer Learning in Evolutionary Computation
CEC-9  Special Session on Evolutionary Computer Vision
CEC-10  Special Session on Evolutionary Computation for Service-Oriented Computing
CEC-11  Special Session on Advanced Evolutionary Computation Approaches for Smart Grid and Sustainable Energy Systems
CEC-12: Special Session on Evolutionary Scheduling and Combinatorial Optimization
CEC-13: Special Session on Evolutionary Bilevel Optimization
CEC-14: Special Session on Evolutionary Methods in Real-world Machine Learning: Non-standard Problems, Big Data and Applications
CEC-15: Special Session on Nature-Inspired Constrained Optimization
CEC-16: Special Session on When Evolutionary Computation Meets Data Mining
CEC-17: Special Session on Evolutionary Algorithms for Sparse Optimization
CEC-18: Special Session on Evolutionary Computation in Healthcare Industry
CEC-19: Special Session on Evolutionary Computation in Dynamic and Uncertain Environments
CEC-20: Special Session on Real-World and Industry Applications of Evolutionary Computation
CEC-21: Special Session on Evolutionary Computation for Complex Optimization in the Energy Domain
CEC-22: Special Session on Recent Advances in Evolutionary Computation for Permutation Problems
CEC-23: Special Session on Evolutionary Computation for Communication Networks
CEC-24: Special Session on Swarm Intelligence in Operations Research, Management Science and Decision Making
CEC-25: Special Session on Fireworks Algorithm and Its Applications
CEC-26: Special Session on Intelligent Transportation and Logistics Networks
CEC-27: Special Session on Fitness Landscape Analysis in Practice
CEC-28: Special Session on Differential Evolution: Past, Present and Future
CEC-29: Special Session on Brain Storm Optimization Algorithms
CEC-30: Special Session on Big Optimization
CEC-31: Special Session on Evolutionary Optimization for Non-Convex Machine Learning
CEC-32: Special Session on Theoretical Foundations of Bio-inspired Computation
CEC-33: Special Session on Artificial Immune Systems: Algorithms, Simulation, Modelling & Theory
CEC-34: Special Session Associated with Competition on (Bound-) Constrained Single Objective Numerical Optimization
CEC-35: Special Session on Mathematical and Algorithmic Frameworks for Simulation of Swarm Intelligence
CEC-36: Special Session on Parallel and Distributed Evolutionary Computation in the Inter-Cloud Era
CEC-37: Special Session on Diversity Preservation Mechanisms for Population-based Meta-heuristics
CEC-38: Special Session on Hybrid Cultural Algorithms: Beyond Classical Cultural Algorithms
CEC-39: Special Session on Deep Neuroevolution
CEC-40: Special Session on Evolutionary Optimization Methods in Energy Internet
CEC-41: CEC-41 Special Session on Large-Scale Global Optimization
CEC-42 Special Session on Dynamic Multi-objective Optimization
CEC-43 Special Session on Advances in Decomposition-based Evolutionary Multi-objective Optimization
CEC-44 Special Session on Evolutionary Methods and Machine Learning in Software Engineering, Testing and SE Repositories

CEC-1  Special Session on Many-Objective Optimization

Organized by Ran Cheng (ranchengcn@gmail.com), Miqing Li, Rui Wang, Xin Yao

Website: https://www.cs.bham.ac.uk/~chengr/CEC_SS_on_MaOO/2018/webpage.html

The field of multi-objective optimization has developed rapidly over the last 20 years, but the design of effective algorithms for addressing problems with more than three objectives (called many-objective optimization problems, MaOPs) remains a great challenge. First, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimization, results in the underperformance of traditional Pareto-based algorithms. Also, the aggravation of the conflict between convergence and diversity, along with increasing time or space requirement as well as parameter sensitivity, has become key barriers to the design of effective many-objective optimization algorithms. Furthermore, the infeasibility of solutions’ direct observation can lead to serious difficulties in algorithms’ performance investigation and comparison. All of these suggest the pressing need of new methodologies designed for dealing with MaOPs, new performance metrics and test functions tailored for experimental and comparative studies of many-objective optimization algorithms.

Scope and Topics

We welcome high-quality original submissions addressing various topics related to many-objective optimization, but are not limited to:

  • Algorithms for many-objective optimization, including search operators, mating selection, environmental selection and population initialization;
  • Performance indicators for many-objective optimization;
  • Benchmark functions for many-objective optimization;
  • Visualization techniques for many-objective optimization;
  • Objective reduction techniques for many-objective optimization;
  • Preference articulation and decision making methods for many-objective optimization;
  • Constraint handling methods for many-objective optimization;
  • Many-objective optimization in combinatorial/discrete problems;
  • Many-objective optimization in dynamic environments;
  • Many-objective optimization in large-scale problems.

CEC-2  Special Session on Memetic Computing

Organized by Liang Feng (liangf@cqu.edu.cn), Chuan-Kang Ting, Maoguo Gong, Meng-Hiot Lim.

Website: http://memecs.org/mcw/SpecialSession_CEC_WCCI_2018.html

Memetic Computing (MC) represents a broad generic framework using the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving. In the literature, MC has been successfully manifested as memetic algorithm, where meme has been typically perceived as individual learning procedures, adaptive improvement procedures or local search operators that enhance the capability of population-based search algorithms. More recently, novel manifestations of meme in the forms such as knowledge building-block, decision tree, artificial neural works, fuzzy system, graphs, etc., have also been proposed for efficient problem-solving. These meme-inspired algorithms, frameworks, and paradigms have demonstrated with considerable success in various real-world applications.

Scope and Topics

The aim of this special session on memetic computing is to provide a forum for researchers in this field to exchange the latest advances in theories, technologies, and practice of memetic computing. The scope of this special session covers, but is not limited to:

  • Single/Multi-Objective memetic algorithms for continuous or combinatorial optimization.
  • Theoretical studies that enhance our understandings on the behaviors of memetic computing.
  • Adaptive systems and meme coordination.
  • Novel manifestations of memes for problem-solving.
  • Cognitive, Brain, individual learning, and social learning inspired memetic computation
  • Self-design algorithms in memetic computing.
  • Memetic frameworks using surrogate or approximation methods
  • Memetic automaton, cognitive and brain-inspired agent based memetic computing
  • Data mining and knowledge learning in memetic computation paradigm
  • Memetic computing for expensive and complex real-world problems
  • Evolutionary multi-tasking

CEC-3  Special Session on Evolutionary Robotics

Organized by Renan C. Moioli (moioli@isd.org.br), Patricia A. Vargas, Micael Couceiro, Josh Bongard, and Phil Husbands

Website: https://sites.google.com/a/isd.org.br/cec18er/

Evolutionary Robotics (ER) aims to apply evolutionary computation techniques to automatically design the control and/or hardware of both real and simulated autonomous robots. Its origins date back to the beginning of the nineties and since then it has been attracting the interest of many research centers all over the world. ER techniques are mostly inspired by existing biological architectures and Darwin’s principle of selective reproduction of the fittest. Evolution has revealed that living creatures are able to accomplish complex tasks required for their survival, thus embodying cooperative, competitive and adaptive behaviors.

Having an intrinsic interdisciplinary character, ER has been employed towards the development of many fields of research, among which we can highlight neuroscience, cognitive science, evolutionary biology, and robotics. Hence, the objective of this special session is to assemble a set of high-quality original contributions that reflect and advance the state-of-the-art in the area of Evolutionary Robotics, with an emphasis on the cross-fertilization between ER and the aforementioned research areas, ranging from theoretical analysis to real-life applications.

Scope and Topics

  • Evolution of robots that display minimal cognitive behavior, learning, memory, spatial cognition, adaptation or homeostasis.
  • Evolution of neural controllers for robots, aimed at giving an insight to neuroscientists, evolutionary biologists or advancing control structures.
  • Evolution of communication, cooperation, and competition, using robots as a research platform.
  • Co-evolution and the evolution of collective behavior.
  • Evolution of morphology in close interaction with the environment, giving rise to self-reconfigurable, self-designing, self-healing, self-reproducing, humanoid and walking robots.
  • Evolution of robot systems aimed at real-world applications as in aerial robotics,    space exploration,    industry,    search and rescue,    robot companions, entertainment, and games.
  • Evolution of controllers on board real robots or the real-time evolution of robot hardware.
  • Novel or improved algorithms for the evolution of robot systems.

The use of evolution for the artistic exploration of robot design.

CEC-4  Special Session on New Directions in Evolutionary Machine Learning

Organized by Yusuke Nojima (nojima@cs.osakafu-u.ac.jp), Will Browne, Masaya Nakata

Website: https://sites.google.com/site/cec2018ssndeml/

Evolutionary Machine Learning (EML) explores technologies that integrate machine learning (e.g., neural network, decision tree, fuzzy systems, reinforcement learning) with evolutionary computation for tasks including optimization, classification, regression, and clustering. Since machine learning contributes to parameter learning while evolutionary computation contributes to model/parameter optimization, one of the fundamental interests in EML is a management of interactions between learning and evolution to produce a system performance that cannot be achieved by either of these approaches alone. Historically, this research area was called Genetics-Based Machine Learning (GBML) and it was concerned with learning classifier systems (LCS) with its numerous implementations. More recently, EML has emerged as a more general field than GBML. It is consequently a broader, more flexible and more capable paradigm than GBML.

Scope and Topics

The aim of this special session is to explore potential EML technologies and clarify new directions for EML to show its prospects. For this purpose, this special session focuses on, but is not limited to, the following areas in EML:

  • Evolutionary learning systems (e.g., learning classifier systems)
  • Evolutionary neural network (e.g., neuroevolution, evolutionary deep neural network)
  • Evolutionary decision tree
  • Evolutionary cascade systems
  • Evolutionary fuzzy systems
  • Evolutionary reinforcement learning
  • Evolutionary ensemble systems
  • Evolutionary adaptive systems
  • Artificial immune systems
  • Genetic programming applied to machine learning
  • Evolutionary feature selection and construction for machine learning
  • Transfer learning; learning blocks of knowledge (memes, code, etc.)
  • Accuracy-interpretability tradeoff in EML
  • Applications and theory of EML

CEC-5  Special Session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction

Organized by Bing Xue (bing.xue@ecs.vuw.ac.nz), Yaochu Jin, and Mengjie Zhang

Website: http://homepages.ecs.vuw.ac.nz/~xuebing/CallforPapers/cec2018Feature.html

In machine learning and data mining, the quality of the input data determines the quality of the output (e.g. accuracy), known as the GIGO (Garbage In, Garbage Out) principle. For a given problem, the input data of a learning algorithm is almost always expressed by a number of features (attributes or variables). Therefore, the quality of the feature space is a key for the success of any machine learning and data algorithm.

Feature selection, feature extraction or construction and dimensionality reduction are important and necessary data pre-processing steps to increase the quality of the feature space, especially with the trend of big data. Feature selection aims to select a small subset of important (relevant) features from the original full feature set. Feature extraction or construction aims to extract or create a set of effective features from the raw data or create a small number of (more effective) high-level features from (a large number of) low-level features. Dimensionality reduction aims to reduce the dimensionality of the data space with the focus of solving “the curse of dimensionality” issue. All of them can potentially improve the performance of a learning algorithm significantly in terms of the accuracy, increase the learning speed, and the complexity and the interpretability of the learned models.  However, they are challenging tasks due to the large search space and feature interaction problems. Recently, there has been increasing interest in using evolutionary computation techniques to solve these tasks due to the fast development of evolutionary computation and capability of stochastic search, constraint handling and dealing with multiple conflict objectives.

Scope and Topics

The theme of this special session is the use of evolutionary computation for feature reduction, covering ALL different evolutionary computation paradigms. The aim is to investigate both the new theories and methods in different evolutionary computation paradigms to feature selection, feature extraction and construction, dimensionality reduction and related studies on improving quality of the feature space and their applications. Authors are invited to submit their original and unpublished work to this special session.  Topics of interest include but are not limited to:

  • Dimensionality reduction
  • Feature ranking/weighting
  • Feature subset selection
  • Multi-objective feature selection
  • Filter, wrapper, and embedded methods for feature selection
  • Feature extraction or construction
  • Single feature or multiple features construction
  • Filter, wrapper, and embedded methods for feature extraction
  • Multi-objective feature extraction
  • Feature selection, extraction, and dimensionality reduction in image analysis, pattern recognition, classification, clustering, regression, and other tasks
  • Feature selection, extraction, and dimensionality reduction on high-dimensional and large-scale data
  • Analysis on evolutionary feature selection, extraction, and dimensionality reduction algorithms
  • Hybridization of evolutionary computation and neural networks, and fuzzy systems for feature selection and extraction
  • Hybridization of evolutionary computation and machine learning, information theory, statistics, mathematical modeling, etc., for feature selection and extraction
  • Real-world applications of evolutionary feature selection and extraction, e.g. images and video sequences/analysis, face recognition, gene analysis, biomarker detection, medical data classification, diagnosis, and analysis, handwritten digit recognition, text mining, instrument recognition, power system, financial and business data analysis, et al.

CEC-6  Special Session on Evolutionary Computing Application in Hardware

Organized by Andy M Tyrrell (andy.tyrrell@york.ac.uk), Martin A Trefzer

Website: http://www-users.york.ac.uk/~mt540/ieee-tf-ehw/wcci2018.html

Evolvable systems encompass understanding, modeling and applying biologically inspired mechanisms to physical systems. Application areas for bio-inspired algorithms include the creation of novel physical devices/systems, novel or optimised designs for physical systems and for the achievement of adaptive physical systems. Having showcased examples from analogue and digital electronics, antennas, MEMS chips, optical systems as well as quantum circuits in the past, we are looking for papers that apply techniques and applications of evolvable systems to these hardware systems, and in particular this year looking for papers in the areas of evolutionary robotics and evolutionary many-core system.

Scope and Topics

The aim of this special session is to provide a forum for the presentation of the latest data, results, and future research directions on bio-inspired computing and hardware. Headline topics are evolvable systems techniques, bio-inspired computation with materials and engineering physical devices, evolutionary many-core systems and evolutionary robotics. The special session invites submissions in any of the following areas:

  • Hardware system optimisation
  • Learning and adaptation
  • Cooperation and competition
  • Co-evolution of robot morphologies
  • Real-world applications
  • Self-reconfigurable systems
  • Adaptive systems
  • Self- repairing systems
  • Fault-tolerant systems
  • Autonomous systems
  • Specialised hardware
  • Computational hardware and materials
  • Self-adaptation
  • Self-monitoring
  • Self-testing

CEC-7  Special Session on Data-Driven Evolutionary Optimization of Computationally Expensive Problems

Organized by Chaoli Sun, Jonathan Fieldsend, Yew-Soon Ong

Meta-heuristic algorithms, including evolutionary algorithms and swarm optimization, face challenges when solving time-consuming problems, as typically these approaches require thousands of function evaluations to arrive at solutions that are of reasonable quality. Surrogate models, which are computationally cheap, have in recent years gained popularity in assisting meta-heuristic optimization, by replacing the compute-expense/time-expensive problem during phases of the heuristic search. However, due to the curse of dimensionality, it is very difficult, if not impossible to train accurate surrogate models. Thus, appropriate model management techniques, memetic strategies, and other schemes are often indispensable. In addition, modern data analytics involving advance sampling techniques and learning techniques such as semi-supervised learning, transfer learning, and active learning are highly beneficial for speeding up evolutionary search while bringing new insights into the problems of interest.

Scope and Topics

This special session aims at bringing together researchers from both academia and industry to explore future directions in this field. The topics of this special session include but are not limited to the following topics:

  • Surrogate-assisted evolutionary optimization for computationally expensive problems
  • Adaptive sampling using machine learning and statistical techniques
  • Surrogate model management in evolutionary optimization
  • Data-driven optimization using big data and data analytics
  • Knowledge acquisition from data and reuse for evolutionary optimization
  • Computationally efficient evolutionary algorithms for large scale and/or many-objective optimization problems
  • Real world applications including multidisciplinary optimization

CEC-8  Special Session on Transfer Learning in Evolutionary Computation

Organized by Bing Xue (Bing.Xue@ecs.vuw.ac.nz), Liang Feng, Yew Soon Ong, Mengjie Zhang

Website: http://homepages.ecs.vuw.ac.nz/~xuebing/CallforPapers/cec2018TLEC.html

Data mining, machine learning, and optimisation algorithms have achieved promises in many real-world tasks, such as classification, clustering, and regression. These algorithms can often generalize well on data in the same domain, i.e. drawn from the same feature space and with the same distribution. However, in many real-world applications, the available data are often from different domains. For example, we may need to perform classification in one target domain, but only have sufficient training data in another (source) domain, which may be in a different feature space or follow a different data distribution. Transfer learning aims to transfer knowledge acquired in one problem domain, i.e. the source domain, onto another domain, i.e. the target domain. Transfer learning has recently emerged as a new learning framework and hot topic in data mining and machine learning.

Scope and Topics

Evolutionary computation techniques have been successfully applied to many real-world problems and started to be used to solve transfer learning tasks. Meanwhile, transfer learning has attracted increasing attention from many disciplines and has been used in evolutionary computation to address complex and challenging issues. The theme of this special session is transfer learning in evolutionary computation, covering ALL different evolutionary computation paradigms, including Genetic algorithms (GAs), Genetic programming (GP), Evolutionary programming (EP), Evolution strategies (ES), Learning classifier systems (LCS), Particle swarm optimization (PSO), Ant colony optimization (ACO), Differential evolution (DE), Evolutionary Multi-objective optimization (EMO) and Memetic computing (MC).

The aim is to investigate in both the new theories and methods on how to transfer learning can be achieved with different evolutionary computation paradigms, and how to transfer learning can be adopted in evolutionary computation and the applications of evolutionary computation and transfer learning in real-world problems.  Authors are invited to submit their original and unpublished work to this special session. Topics of interest include but are not limited to:

  • Evolutionary supervised transfer learning
  • Evolutionary unsupervised transfer learning
  • Evolutionary semi-supervised transfer learning
  • Domain adaptation and domain generalization in evolutionary computation
  • Instance-based transfer approaches in evolutionary computation
  • Feature-based transfer learning in evolutionary computation
  • Parameter/model based transfer learning in evolutionary computation
  • Relational-based transfer learning in evolutionary computation
  • Transfer learning in evolutionary computation for classification
  • Transfer learning in evolutionary computation for regression
  • Transfer learning in evolutionary computation for clustering
  • Transfer learning in evolutionary computation for other data mining tasks, such as association rules and link analysis
  • Transfer learning in evolutionary computation for numeric optimisation tasks
  • Transfer learning in evolutionary computation for scheduling and combinatorial optimisation tasks
  • Hybridization of evolutionary computation and neural networks, and fuzzy systems and memetic computing for transfer learning
  • Hybridization of evolutionary computation and machine learning, information theory, statistics, etc., for transfer learning
  • Transfer learning in evolutionary computation for real-world applications, e.g. text mining, image analysis, face recognition, WiFi localization, etc.

CEC-9  Special Session on Evolutionary Computer Vision

Organized by Mengjie Zhang (mengjie.zhang@ecs.vuw.ac.nz), Vic Ciesielski, Mario Köppen

Website: http://homepages.ecs.vuw.ac.nz/~mengjie/ecv18.html

Computer vision is a major unsolved problem in computer science and engineering. Over the last decade, there has been increasing interest in using evolutionary computation approaches to solve vision problems. Computer vision provides a range of problems of varying difficulty for the development and testing of evolutionary algorithms. There have been a relatively large number of papers in evolutionary computer vision in recent CEC and GECCO conferences. It would be beneficial to researchers to have these papers in a special session. Also, a special session would encourage more researchers to continue to work in this field and consider CEC a place for presenting their work.

Scope and Topics

The proposed special session aims to bring together theories and applications of evolutionary computation to computer vision and image processing problems. Topics of interest include, but are not limited to:

New theories and methods in different EC paradigms  for computer vision and image processing including

  • Evolutionary algorithms such as genetic algorithms, genetic programming, evolutionary strategies and evolutionary programming;
  • Swarm Intelligence methods such as particle swarm optimisation, ant colony optimisation, and artificial bee colony optimisation;
  • Emergent, metaheuristics and other EC approaches such as learning classifier systems, differential evolution, artificial immune systems, multi-objective optimisation, hybrid search and memetic computing, transfer learning and domain adaptation, deep learning and kernel methods, hyper-heuristic techniques; and
  • Cross-fertilization of evolutionary computation with other techniques such as neural networks including deep learning and fuzzy systems is also encouraged. New methods in evolutionary deep learning and evolutionary fuzzy deep learning for image and vision computing are highly welcome.

Applications in computer vision and image processing including

  • Edge detection in noisy images
  • Image segmentation in biological images
  • Automatic feature extraction, construction, and selection in complex images
  • Object identification and scene analysis for medical applications
  • Object detection and classification in security scenarios
  • Handwritten digit recognition and detection
  • Vehicle plate detection
  • Face detection and recognition
  • Texture image analysis
  • Automatic target recognition in military services
  • Gesture identification and recognition
  • Robot vision

CEC-10  Special Session on Evolutionary Computation for Service-Oriented Computing

Organized by Hui Ma, Yi Mei (yi.mei@ecs.vuw.ac.nz), and Mengjie Zhang

Service-oriented computing is becoming more and more prominent in the Internet environment with the rapid growth of Web services available on the internet. This raises issues for Web service providers such as Web service composition and location allocation, resource allocation and scheduling, etc. Furthermore, there are multiple potentially conflicting objectives (called Quality-of-Service, QoS) to be considered simultaneously in the problem such as response time, cost, reliability, safety, etc. In the era of cloud computing and Big Data, the number and complexity of Web services on the Internet is increasing rapidly. Traditional service composition approaches have come to a performance bottleneck.

Computational Intelligence (CI) has been successfully applied to many challenging real-world problems. This special session aims to solve the service-oriented computing problems with CI techniques, covering all different evolutionary computation paradigms such as Genetic Algorithms (GAs), Genetic Programming (GP), Evolutionary Programming (EP), Evolution Strategies (ES), Memetic Algorithms (MAs), Learning Classifier Systems (LCS), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Differential Evolution (DE), and Evolutionary Multi-objective Optimization (EMO), as well as neural networks and fuzzy systems.

Scope and Topics

The scope of this special session includes both new theories and methods on how to solve the challenging service-oriented computing problems such as Web service composition and location-allocation more effectively and efficiently. Authors are invited to submit their original and unpublished work to this special session.

Topics of interest include, but not limited to:

  • Evolutionary Web service composition
  • Evolutionary Web service workflow optimisation
  • Evolutionary Web service selection
  • Evolutionary Web service location allocation
  • Evolutionary Web service scheduling
  • Evolutionary semantic Web service composition
  • Evolutionary dynamic Web service composition
  • Multi-objective Web service composition
  • Evolutionary computation for resource allocation in Cloud computing
  • Evolutionary computation for workflow management in Cloud
  • Evolutionary computation for distributed Web service composition
  • Novel representations and search operators for Service-oriented computing
  • Cooperative coevolution for Service-oriented computing
  • Neural networks for service-oriented computing
  • Fuzzy systems for service-oriented computing
  • Hybrid algorithms between EC, neural networks and fuzzy systems for service-oriented computing

CEC-11  Special Session on Advanced Evolutionary Computation Approaches for Smart Grid and Sustainable Energy Systems

Organized by: Zhi-le Yang (zl.yang@siat.ac.cn), Jing J. Liang and Kang Li

To shape a low carbon energy future has been a crucial and urgent task under Paris Global Agreement. Numerous optimisation problems have been formulated and solved to effectively save the fossil fuel cost and relief energy waste. However, the majority of the problems are of strong non-convex and non-smooth characteristics. Evolutionary computation is promising to provide powerful optimisation tools for intelligently and efficiently solving problems such as power and sustainable energy systems scheduling to reduce carbon consumptions.

Scope and Topics

This special session intends to bring together the state-of-the-art advances in evolutionary optimisation approaches for solving emerging problems in complex modern power and sustainable energy system. The submissions are encouraged to be the focus on smart grid scheduling with the integration of new participants such as renewable generations, plug-in electric vehicles, distribution generations and energy storages, multiple time-spacial energy reductions and other energy optimisation topics.

Potential submission topics include not but not limited to the below ones:

  • Unit commitment, economic dispatch, and optimal power flow
  • Optimal smart grid scheduling and integration with renewable energy generations
  • Intelligent coordination and control of plug-in electric vehicles
  • Efficient powertrain management for hybrid electric vehicles
  • Charging and discharging strategies for energy storage battery systems
  • Internal and whole scale management for single and hybrid energy storage systems
  • Energy reduction strategies for energy-intensive manufacturing processes
  • Parameters identification for photovoltaic models and PEM fuel cells
  • Thermodynamic optimisation for heat exchanger design and Organic Rankine Cycle

 

CEC-12  Special Session on Evolutionary Scheduling and Combinatorial Optimization

Organized by Su Nguyen (P.Nguyen4@latrobe.edu.au), Yi Mei, Gang (Aaron) Chen, and Mengjie Zhang

Evolutionary scheduling and combinatorial optimisation (ESCO) is an important research area at the interface of artificial intelligence (AI) and operations research (OR). ESCO has attracted the attentions of researchers over the years due to its applicability and interesting computational aspects. Evolutionary Computation (EC) techniques are suitable for these problems since they are highly flexible regarding handling constraints, dynamic changes, and multiple conflicting objectives. With the growth of new technologies and business models, researchers in this field have to continuously face new challenges, which required innovated solution methods.

Scope and Topics

This special session focuses on both practical and theoretical aspects of Evolutionary Scheduling and Combinatorial Optimization. Examples of evolutionary methods include genetic algorithm, genetic programming, evolutionary strategies, ant colony optimisation, particle swarm optimisation, evolutionary based hyper-heuristics, memetic algorithms. Novel hybrid approaches that combine machine learning and evolutionary computation to solve difficult ESCO problems are highly encouraged. Examples include using machine learning to improve surrogate-assisted evolutionary algorithms, and designing evolutionary algorithms for reinforcement learning and transfer learning.

We welcome the submissions of quality papers that effectively use the power of EC techniques to solve hard and practical scheduling and combinatorial optimization problems. Papers with rigorous analyses of EC techniques and innovative solutions to handle challenging issues in scheduling and combinatorial optimisation problems are also highly encouraged.

Topics of interest include, but not limited to:

  • Production scheduling
  • Timetabling
  • Vehicle routing
  • Project scheduling
  • Airport runway scheduling
  • Transport scheduling
  • Grid/cloud scheduling
  • Evolutionary scheduling with Big Data
  • Web service composition
  • Wireless networking state location allocation
  • Project scheduling
  • 2D/3D strip packing
  • Space allocation
  • Multi-objective scheduling
  • Multiple interdependent decisions
  • Automated heuristic design
  • Innovative applications of evolutionary scheduling and combinatorial optimisation

 

CEC-13  Special Session on Evolutionary Bilevel Optimization

Organized by: Ankur Sinha (asinha@iima.ac.in) and Kalyanmoy Deb

Website: http://bilevel.org/sessionCEC18.html

Bilevel optimization problems are special kind of optimization problems that involve two levels of optimization, namely upper level and lower level. The hierarchical structure of the problem requires that every feasible solution to the upper level problem should satisfy the optimality conditions of the lower level problem. Such a requirement makes bilevel optimization problems difficult to solve. These problems are commonly found in many practical problem solving tasks, which include optimal control, process optimization, game-playing strategy development, transportation problems, coordination of multi-divisional firms, machine learning and others. Due to the computation expense and other difficulties involved in handling such problems, they are often handled using approximate solution procedures. There is a need for theoretical as well as methodological advancements to handle such problems efficiently.

Scope and Topics

IEEE Congress on Evolutionary Computation (CEC) being one of the leading conferences in evolutionary computation will give an opportunity to researchers and practitioners to discuss and exchange ideas for handling bilevel problems, which have yet not been widely explored by the evolutionary computation community. The special session on Evolutionary Bilevel Optimization will bring together researchers working on the following topics:

  • Algorithms for bilevel optimization problems
  • Algorithms for multi-objective bilevel optimization problems
  • Approximate procedures to handle bilevel optimization problems
  • Hybrid approaches to handle bilevel optimization problems
  • Theoretical results on bilevel optimization problems
  • Bilevel Application Problems
  • Hierarchical decision making

 

CEC-14  Special Session on Evolutionary Methods in Real-world Machine Learning: Non-standard Problems, Big Data and ApplicationsOrganized by Mikel Galar, Isaac Triguero (Isaac.Triguero@nottingham.ac.uk) and José María Luna

Website: http://www.cs.nott.ac.uk/~pszit/EvoML.html

Brief Description

The aim of this special session is to serve as a forum for the exchange of ideas and discussions on recent and new trends in complex data mining and machine learning problems with evolutionary methods. Machine learning is a very active research field because of the huge number of real-world applications that can be addressed by this field of research. There are many non-standard problems, besides the canonical classification, regression, clustering or pattern mining, which require special focus and development of novel and effective solutions. Such challenges include the problem of big data, data streams, class-imbalance data, learning on the basis of low quality and noisy examples, multi-label and multi-instance problems, or having limited access to object labels at the training phase, among others.

Evolutionary techniques are widely used to face the aforementioned challenges with promising results. They can be used either in the data processing part (i.e., data reduction or augmentation such as feature and instance selection or feature engineering) or in the learning process (i.e., genetics-based machine learning, evolving ensembles, neural networks or fuzzy systems). Moreover, Big Data scenario opens up new possibilities for Evolutionary methods in machine learning. New challenges arose with the need of effectively processing large amounts of data in reasonable times.

From this viewpoint, the aim of this special session is to explore Evolutionary methods and machine learning in any part of the learning process to address non-standard problems. Real-world applications dealing with non-standard problems with evolutionary methods are also welcomed. We encourage authors to submit original papers as well as preliminary and promising works in the topics of this special session.

Scope and Topics

The aim of the session is to provide a forum for the exchange of ideas and discussions on evolutionary algorithms for machine learning, in order to deal with the current challenges in this topic. The special session is therefore open to high quality submissions from researchers working in learning problems using evolutionary techniques. The topics of this special session include evolutionary models for dealing with non-standard machine learning problems, handling data-level difficulties and improving machine learning methods in areas such as:

  • Big data mining
  • Data streams and concept drift
  • Imbalanced learning
  • Supervised / Unsupervised / Semi-supervised learning
  • Instance Selection / Generation
  • Multi-label / Multi-instance learning
  • Feature and label noise
  • One-class classification / Learning from positive and unlabeled samples
  • Kernels and Support Vector Machines
  • Ensemble learning
  • Fuzzy systems
  • Manifold Learning
  • Real-world applications e.g., in medical informatics, bioinformatics, social networks, biometry, etc.

 

CEC-15  Special Session on Nature-Inspired Constrained Optimization

Organized by Efrén Mezura-Montes (emezura@uv.mx), Helio J.C. Barbosa, and Rituparna Datta

Website: http://www.lania.mx/~emezura/sites/cec2018/index.html

In their original versions, nature-inspired algorithms for optimization such as evolutionary algorithms (EAs) and swarm intelligence algorithms (SIAs) are designed to sample unconstrained search spaces. Therefore, a considerable amount of research has been dedicated to adapt them to deal with constrained search spaces. The objective of the session is to present the most recent advances in constrained optimization using different nature-inspired algorithms.

Scope and Topics

The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:

  • Novel constraint-handling techniques for EAs and SIAs
  • Novel constraint-handling techniques for constrained dynamic optimization
  • Novel/adapted search algorithms for constrained optimization
  • Memetic algorithms in constrained search spaces
  • Parameter setting (tuning and control) in constrained optimization
  • Mixed (discrete-continuous) constrained optimization
  • Theoretical analysis and complexity of algorithms in constrained optimization
  • Convergence analysis in constrained optimization
  • Performance evaluation of algorithms in constrained optimization
  • Expensive Constrained Optimization
  • Design of difficult and scalable test functions
  • Applications

 

CEC-16  Special Session on When Evolutionary Computation Meets Data Mining

Organized by Zhun Fan, Xinye Cai (xinye@nauu.edu.cn), Chuan-Kang Ting

Website: http://www.ixingo.cn/cec.html

Many of the tasks carried out in data mining and machine learning, such as feature subset selection, associate rule mining, model building, etc., can be transformed as optimization problems.  Thus it is very natural that Evolutionary Computation (EC),  has been widely applied to these tasks in the fields of data mining (DM) and machine learning (ML),  as an optimization technique.

On the other hand, EC is a class of population-based iterative algorithms, which generate abundant data about the search space, problem feature and population information during the optimization process. Therefore, the data mining and machine learning techniques can also be used to analyze these data for improving the performance of EC. A plethora of successful applications have been reported, including the creation of new optimization paradigm such as Estimation of Distribution Algorithm,  the adaptation of parameters or operators in an algorithm, mining the external archive for promising search regions, etc.

However, there remain many open issues and opportunities that are continually emerging as intriguing challenges for bridging the gaps between EC and DM. The aim of this special session is to serve as a forum for scientists in this field to exchange the latest advantages in theories, technologies, and practice.

Scope and Topics

We invite researchers to submit their original and unpublished work related to, but not limited to, the following topics:

  • EC Enhanced by Data Mining and Machine Learning Concepts and/or Framework
  • Data Mining and Machine Learning Based on EC techniques
  • Machine Learning Enhanced and/or Model-based Multi- and/or Many-objective Optimization
  • Data Mining and Machine Learning Enhanced Constrained Optimization:
  • Data Mining and Machine Learning Enhanced Memetic Computation or Local Search
  • Data Mining and Machine Learning Enhanced EC for Combinatorial Optimization
  • Data Mining and Machine Learning Enhanced EC for Large-scale Optimization
  • Data Mining and Machine Learning Enhanced EC for Dynamic Optimization
  • Association Rule Mining Based on Multi-Objective Optimization
  • Knowledge Discovery in Data Mining via Evolutionary Algorithm
  • Genetic Programming in Data Mining
  • Multi-Agent Data Mining using Evolutionary Computation
  • Medical Data Mining with Evolutionary Computation
  • Evolutionary Computation in Intelligent Network Management
  • Evolutionary Clustering in Noisy Data Sets
  • Big Data Projects with Evolutionary Computation
  • Deep Learning with Evolutionary Computation
  • Real World Applications

 

CEC-17  Special Session on Evolutionary Algorithms for Sparse Optimization

Organized by Jing Liang (liangjing@zzu.edu.cn), Maoguo Gong, and Hui Li.

Website: http://www5.zzu.edu.cn/ecilab/info/1036/1011.htm

According to compressed sensing theory, an unknown sparse or compressive signal can be recovered from a few measured values, which are much less than those used in previous theories such as Nyquist sampling theorem. Many sparse optimization algorithms have been proposed to solve the sparse optimization problems. Although some comparisons are made in some research studies, they are often limited to the sparse optimization problems used in the study. In some occasions, the proposed algorithms may favor the test problems in their works, but may not so effective on other problems. There is definitely a need of evaluating these algorithms in a more systematic manner on an open and fair competition platform. Therefore a set of sparse optimization test problems of various complexities need to be designed, such as problems with different signal length, various measured values and different sparsities. Some of the designed problems are involved with noise to make it difficult to be solved. Moreover different types of real-world application problems, like magnetic resonance imaging and sparse network optimization are included in the test problems. In addition, a fair and appropriate evaluation criterion is given to assess the performance of different sparse reconstruction algorithms.

Scope and Topics

This special session is devoted to the novel approaches, algorithms and techniques for solving sparse optimization test problems. We encourage all researchers to test their algorithms on the novel CEC’18 test suite. The participants are required to send the final results in the format introduced in the technical report to the organizers and the organizers will present an overall analysis and comparison based on these results. Papers on novel concepts that help us in understanding problem characteristics are also welcome. The special session invites submissions in any of the following areas:

  • Evolutionary algorithms for sparse optimization
  • Evolutionary algorithms for sparse reconstruction
  • Encoding methods for sparse optimization
  • Evaluations of sparse optimization algorithms
  • Related theory analysis
  • Applications

 

CEC-18  Special Session on Evolutionary Computation in Healthcare Industry

Organized by Handing Wang (handing.wang@surrey.ac.uk), Rong Qu, Dujuan Wang, Yaochu Jin

Website: https://sites.google.com/view/ieee-cis-tf-ish/cec-2018-special-session-on-evolutionary-computation-in-healthcare-industry

Worldwide, the healthcare industry would continue to thrive and grow, because diagnosis, treatment, disease prevention, medicine, and service affect the mortal rates and life quality of human beings. Two key issues of the modern healthcare industry are improving healthcare quality as well as reducing economic and human costs. The problems in the healthcare industry can be formulated as scheduling, planning, predicting, and optimization problems, where evolutionary computation methods can play an important role. Although evolutionary computation has been applied to scheduling and planning for trauma system and pharmaceutical manufacturing, other problems in the healthcare industry like decision making in computer-aided diagnosis and predicting for disease prevention have not properly formulated for evolutionary computation techniques, and many evolutionary computation techniques are not well-known to the healthcare community.

Scope and Topics

This special session aims to promote the research on evolutionary computation methods for their application to the healthcare industry. The topics of this special session include but are not limited to the following topics:

  • Evolutionary computation in resource allocation for hospital location planning, aeromedical retrieval system planning, etc.
  • Application of evolutionary computation for job scheduling, such as ambulance scheduling, nurse scheduling, job scheduling in medical device and pharmaceutical manufacturing, etc.
  • Multiple-criteria decision-making for computer-aided diagnosis using expert systems.
  • Web self-diagnostic system with the application of information retrieval and recommendation system.
  • Learning and optimization for vaccine selection and personalized/stratified medicine.
  • Data-driven surrogate-assisted evolutionary algorithms in pharmaceutical manufacturing processes.
  • Modeling and prediction in epidemic surveillance system for disease prevention.
  • Route planning for disability robots.

 

CEC-19  Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE)

Organized by Michalis Mavrovouniotis (michalis.mavrovouniotis@ntu.ac.uk), Changhe Li, and Shengxiang Yang

Website: http://www.tech.dmu.ac.uk/~syang/TF-ECiDUE/ECiDUE18.html

Many real-world optimization problems are subject to dynamism and uncertainties that are often impossible to avoid in practice. For instance, the fitness function is uncertain or noisy as a result of simulation/ measurement errors or approximation errors (in the case where surrogates are used in place of the computationally expensive high-fidelity fitness function). In addition, the design variables or environmental conditions can be perturbed or they change over time.

The tools to solve these dynamic and uncertain optimization problems (DOP) should be flexible, able to tolerate uncertainties, fast to allow reaction to changes and adaptive. Moreover, the objective of such tools is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic environments, or to find a robust solution that operates properly in the presence of uncertainties.

The last decade has witnessed increasing research efforts on handling dynamic and uncertain optimization problems using evolutionary algorithms and other metaheuristics, e.g., ant colony optimization, particle swarm optimization, artificial bee colony etc., and a variety of methods have been reported across a broad range of application backgrounds.

Scope and Topics

This special session aims at bringing together researchers from both academia and industry to review the latest advances and explore future directions in this field. Topics of interest include but are not limited to:

  • Benchmark problems and performance measures
  • Dynamic single – and multi-objective optimization
  • Adaptation, learning, and anticipation
  • Models of uncertainty and their management
  • Handling noisy fitness functions
  • Using fitness approximations
  • Searching for robust optimal solutions
  • Algorithm comparison and benchmarking
  • Hybrid approaches
  • Theoretical analysis
  • Real-world applications

 

CEC-20 Special Session on Real-World and Industry Applications of Evolutionary Computation

Organized by: Amir H Gandomi (a.h.gandomi@stevens.edu), Mohammad Nabi Omidvar, Kalyanmoy Deb

Website: https://wp.me/P6wNuk-3E

During the last three decades, evolutionary computation (EC) has been widely used for solving complex real-world problems. These techniques are getting popular these days for business, management, and design optimization as they are commonly deal with complex problems without explicit formula. The main focus of this special session would be on the EC techniques applications to complex real-world problems.

Management, Scheduling, design, maintenance and monitoring real-world and industrial systems, such as factories and companies, are challenging issues and involve several constraints. In order to find a practical solution, most real-world problems should be formulated as discrete or mixed variable optimization problems. Furthermore, finding efficient and lower cost procedures for frequent use of the system is crucially important. Mining and interpretation of the response data are other major issues that need advanced computation. Stochastic nature of most real-world systems (e.g. stock market) make these analyses even more complex. While several solutions are proposed to tackle the issues mentioned above, there is still a serious need for more cost-effective approaches. Due to their complexity, the real-world problems are difficult to solve using derivative-based and local optimization algorithm. A viable solution to cope with this limitation is to employ global optimization algorithms, such as the EC techniques. In the recent past, EC and its branches have been used to solve complex real-world problems that cannot be solved using conventional methods. The other important issue is that several aspects can be considered to optimize systems simultaneously such as time, cost, quality, risk, etc. Therefore, more than one objective should usually be considered for optimizing a real-world system. This is while there are usually conflicts between the considered objectives, such as cost-quality. In this case, the multi-objective optimization concept offers major advantages over the traditional mathematical algorithms. More specifically, evolutionary multi-objective optimization (EMO) is known as a reliable way to handle these problems in the industrial domain.

Scope and Topics

This special session strives to gather the latest development of EC applications in real-world systems. On this basis, this special session includes key applications of EC on different disciplines such as business, management, engineering optimization, etc. Topics to be included are evolutionary optimization and multi-objective algorithms, as well as evolutionary (big) data mining algorithms. The methods of interest in real-world domains include (but not limited to):

  • Operation management
  • Planning and Scheduling problem
  • Maintenance and Monitoring optimization
  • Design optimization (topology, configuration, etc.)
  • Optimizing transportation systems
  • Stock market prediction
  • Portfolio Optimization
  • Layout problem
  • Simulation optimization (grey/black box problems)
  • Large-scale real-world systems
  • Multi and many objective real-world problems
  • Expensive real-world problem (limited budget)
  • Surrogate-assisted systems
  • Highly constrained problems
  • Embedding knowledge
  • Robust real-world optimization
  • Probabilistic real-world optimization
  • Bi-level real-world optimization
  • real-world (big) data mining
  • Uncertain and noisy systems

 

CEC-21 Special Session on Evolutionary Computation for Complex Optimization in the Energy Domain

Organized by Joao Soares (joaps@isep.ipp.pt), Fernando Lezama, Zita Vale

Website: http://www.gecad.isep.ipp.pt/IEEE-SS-CEC-WCCI2018/

Increasing energy demand and limited world resources call for sustainability, which is critical to keep up the world at the current pace. Efficiency is very relevant to contribute to this sustainability, and adequate methods of energy production and consumption are highly relevant. Optimization approaches are a crucial part of the planning, operation, and control of energy systems. However, many optimization problems in the energy domain are complex by nature since they are highly constrained and face issues related to high-dimensionality, lack of information, noisy and corrupted data as well as real-time requirements. Under these conditions, it becomes difficult to find a solution in an adequate amount of time. Even the most sophisticated exact solutions require workarounds that often lead to unsatisfactory performance and applicability of the algorithms.

Due to the difficulties of traditional algorithms to find feasible solutions for those complex problems in real-world conditions, Evolutionary Computation (EC) has emerged and demonstrated satisfactory performance in a wide variety of applications in the energy domain.

Scope and Topics

This special session welcomes research work concerning real-world applications of EC in the energy domain. The problems can be focused in different parts of the energy chain (e.g., heating, cooling, and electricity supply) and different consumer targets (e.g., residential or industrial level). Problems dealing with uncertainty, dynamic environments, and large-scale search spaces are a plus to the aim of this special session. This special session aims at bringing together the latest applications of EC to optimization problems in the energy domain.

Topics must be related to EC in the energy domain including:

  • Electric and plug-in hybrid vehicles
  • Electricity markets
  • Energy scheduling
  • Heat and electricity joint optimization problems
  • Hydrogen economy problems
  • Multi-objective problems in the energy domain
  • Natural gas optimization problems
  • Optimal power flow in distribution and transmission
  • Residential, industrial and district cooling/heating problems
  • Smart grid and micro-grid problems
  • Solar and wind power integration and forecast
  • Super grids problems (continental and trans-continental transmission system)
  • Transportation & energy joint problems

 

CEC-22 Special Session on Recent Advances in Evolutionary Computation for Permutation Problems

Organized by Josu Ceberio (josu.ceberio@ehu.eus), Olivier Regnier-Coudert, Valentino Santucci

Websitehttp://www.sc.ehu.es/ccwbayes/cec2018_permutations/

Overview

Permutation-based optimization problems are a class of combinatorial optimization problems that naturally arises in many real world applications and theoretical scenarios where an optimal ordering or ranking of items has to be found with respect to one or more objective criteria. Some popular examples are: flowshop scheduling problem, traveling salesman problem, quadratic assignment problem and linear ordering problem.

Since the first paper on the traveling salesman problem in 1985 by Goldberg, permutation problems have been recurrently addressed in the field of Evolutionary Computation (EC) from a wide variety of perspectives. Evolutionary algorithms, fitness landscape analysis, genotypic representations or probabilistic modeling on rankings are only a few of the topics that have been discussed in the literature. In modern combinatorics, permutations are probably among the richest combinatorial structures. Motivated principally by their versatility – ordered set of items, collection of disjoint cycles, transpositions, matrices or graphs – permutations appear in a vast range of domains, thus making permutation problems a very special case where ideas and concepts originated from classical mathematic fields, such as algebra, geometry, and probability theory, can be exploited and used in the design of new metaheuristics and genetic operators.

All these aspects have recently motivated a strong and ongoing research interest towards permutation problems in EC. Therefore, this special session aims to highlight the most recent advances in the field and to bring together the EC researchers working in all the aspects of permutation problems.

Scope and Topics

Authors are invited to submit their original and unpublished work in the areas including, but not limited to:

  • EC applications to the flowshop scheduling problem
  • EC applications to the traveling salesman problem
  • EC applications to the linear ordering problem
  • EC applications to the quadratic assignment problem
  • EC applications to any kind of single or multiple objective(s) permutation-based optimization problem
  • Novel permutation-based optimization problems in EC
  • Fitness landscape analysis of permutation-based optimization problems
  • Theoretical analysis of permutation search spaces, meta-heuristics and hardness of problem instances
  • Algebraic models for EC in permutation-based search spaces
  • Probabilistic models for EC in permutation-based search spaces
  • Permutation genotypic representations for EC techniques
  • Experimental evaluations and comparisons of EC techniques for permutation-based optimization problems

 

CEC-23 Special Session on Evolutionary Computation for Communication Networks

Organized by Hui Cheng (H.Cheng@ljmu.ac.uk), Chuan-Kang Ting, and Shengxiang Yang

Communication Networks have evolved dramatically over the past decades and become an Infrastructure of Strategic Importance. Today communication networks are essential for all areas and sectors of our societies and economies around the globe. They support the many services and applications of the Internet. The fast development of network infrastructures, both fixed and mobile, has been influenced by the growing demand for data communications. The impact of optimization in communication networks, such as Internet and mobile wireless networks, on the modern economy and society has been growing steadily. At the present, new technologies like 5G cellular mobile radio systems, optical Internet, and network virtualization and automation are in widespread use, allowing fast data communications, new services and applications. With the advent of computer systems, computational intelligence approaches have been developed for systematic design, optimization, and improvement of different communication network systems.

Scope and Topics

The aim of the special session is to promote research and reflect the most recent advances of evolutionary computation, including evolutionary algorithms, deep learning, neural network, fuzzy systems, metaheuristic techniques and other intelligent methods, in the solution of problems in communication networks.

Topics of interest include, but are not limited to:

  • Communication network systems:
    telecommunications; mobile, satellite, and optical communications; switching and routing; network functions virtualization (NFV)and software-defined networking (SDN); communication systems simulation; station and antenna design; frequency and channel assignment; information and speech processing; intrusion detection; error control coding; compression and cryptography; propagation and channel modeling, protocol design, etc.
  • General network problems:
    parallel and distributed systems; networks and graph problems; unconstrained and constrained network design problems; structural and computational complexity; adaptability to environmental variations; robustness to network changes and failures; effectiveness and scalability of performance; location and link design; reliability and failure; location placement; network physical and software architecture; network hardware and software technologies; operations, maintenance, and management; signaling and control; active networks; network services and applications, etc.

 

CEC-24 Special Session on Swarm Intelligence in Operations Research, Management Science and Decision Making

Organized by Wei-Chang Yeh (yeh@ieee.org), Yew Soon Ong, and Vera Yuk Ying Chung

Swarm Intelligence is an important modern computational intelligence in artificial intelligence and computer science. The essential idea of swarm intelligence algorithms is primarily as a result of information being updated via repeated interactions between individuals to optimize cognitive performance in groups that emerges from self-organized decision making. That is, Swarm Intelligence is the emergent collective intelligence of groups of simple agents. There are several popular algorithms based on these concepts, including Genetic algorithm (GA), Memetic Algorithm (MA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) algorithms, Simplified Swarm Optimization (SSO), and many other flavors. Today, artificial intelligence and computer science has become an increasingly important driving role on the developments and applications in the real world. The complex systems, which comprise a large number of parts that has interaction with each other to yield nontrivial solutions, have rapid development. Many real systems have been well recognized as complex systems, such as the human body, engineering system, social society, traffic, and atmosphere. The increasing complexities of the problems that we attempt to deal with today have led to new emerging challenges pertaining to design, implementation and evaluation of reliable complex systems. Therefore, swarm intelligence algorithms play a major role to be proposed for optimization problems of advanced complex systems.

It is recognized the Swarm Intelligence have been a popular research area that has received significant attention during the past several decades because of Swarm Intelligence’s critical importance in various kinds of fields. In recent years, we have seen an increasing interest in Swarm Intelligence in creating stochastic methodologies and optimization techniques with the aims of resembling and simulating the phenomenon of nature for solving larger problems in Operations Research, Management Science and Decision Making including multi-objective optimization, industrial engineering optimization problems, computer engineering optimization problems, deep learning, granular computing, power & energy design problems, scheduling problems, traffic design, logistic problems, advanced transportation problems, network design and routing, manufacturing design, and reliability design problems.

Scope and Topics

Despite a significant amount of research on Swarm Intelligence, there remain many open issues and intriguing challenges in the field. The aims of this special session are to strengthen the collaboration of Swarm Intelligence in Operations Research, Management Science and Decision Making, to discuss and exchange the latest advances in Swarm Intelligence, and to explore the future directions in Swarm Intelligence. Authors are invited to submit their original and unpublished work in the areas including, but not limited to:

  • Swarm Intelligence
  • Swarm Intelligence in Operations Research, Management Science or Decision Making
  • Advanced model of Swarm Intelligence
  • Data Mining using Swarm Intelligence
  • Analytical studies that enhance our understanding on the behaviors of Swarm Intelligence
  • The optimization techniques of Swarm Intelligence
  • Knowledge incorporation in Swarm Intelligence

 

CEC-25 Special Session on Fireworks Algorithm and Its Applications

Organized by Ying Tan (ytan@pku.edu.cn) and Hideyuki Takagi

Website: http://www.cil.pku.edu.cn/FWA_CEC/index.html

Fireworks Algorithm (FWA) has become one of the promising swarm intelligence algorithms in recent years, and received extensive attentions from many researchers and practitioners, because it has shown a great success in solving many complex optimization problems, especially for multi-modal optimization problems frequently happened in a lot of real-world applications. Compared with many current SI algorithms, FWA is of a new explosive search manner and probably has a fine structure of search in the solution space. As a result, it shows a strong capability of optimization computation in many optimization problems. Till to date, it has many effective variants and huge amount of successful applications. Furthermore, FWA is suitable for parallelization and works significantly better than other SI algorithms.

The aim of this special session is to bring together the experts, active researchers and newcomers from either academia or industry over the world to discuss some important issues of fireworks algorithm and its progress. All of the latest work and achievements related FWA are all welcome to this special session under the umbrella of the IEEE Congress of Evolutionary Computation at the IEEE WCCI’2018.

Scope and Topics

Full papers are invited on recent advances in the development of FWA, i.e., FWA improvements and applications. The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:

  • Theoretical analysis of FWA
  • Algorithmic improvements of FWA
  • FWA for single-, multi-, and many-objective optimization
  • FWA for data mining
  • FWA for machine learning
  • FWA for data analysis
  • Parallelized realizations of FWA
  • Distributed realizations of FWA
  • Applications of FWA

 

CEC-26 Special Session on Intelligent Transportation and Logistics Networks

Organized by Chuan-Kang Ting (ckting@cs.ccu.edu.tw), Hui Cheng, Shengxiang Yang, and Rung-Tzuo Liaw

Transportation and logistics serve as two important tasks in modern human life and industry activities. Optimization of intelligent transportation and logistics networks has shown to be a difficult problem. The worldwide division of labor, the connection of distributed centers, and the increased mobility of individuals, furthermore, lead to an increased demand for efficient solutions to solve the problems in transportation and logistics networks. Evolutionary computation plays a significant role and has gained promising results in optimization of transportation and logistics networks.

Scope and Topics

The aim of this special session is to promote research and reflect the most recent advances of evolutionary computation in the solution of problems in transportation and logistics networks.

Topics of interest include, but are not limited to:

  • Transportation and supply networks
  • Logistics optimization
  • Supply chain management
  • Freight and passenger services
  • Pickup and delivery
  • Flight and crew scheduling
  • Tracking and tracing
  • Fleet and order management
  • Modeling and traffic management
  • Traffic simulation
  • Individual and public transportation
  • Inventory optimization
  • Routing and scheduling

 

CEC-27 Special Session on Fitness Landscape Analysis in Practice

Organized by Katherine M. Malan (malankm@unisa.ac.za) and Marie-Eléonore Kessaci

Website: http://www.kmalan.co.za/wcci2018_ss/

Since the notion of a fitness landscape was introduced by Sewell Wright in 1932, fitness landscapes have been studied by evolutionary biologists to better understand how evolution occurs in nature. In a similar way, researchers in evolutionary computation (EC) have used fitness landscapes to better understand the evolutionary process of search. Studies have ranged from theoretical models of fully enumerated combinatorial landscapes to the prediction of algorithm performance based on approximate fitness landscape characteristics. Fitness landscape analysis is a growing field in the EC community, but research has been scattered in widely different publications and conferences. Research papers in fitness landscapes are often incorporated into theoretical tracks of conferences, even when the research is focused on the practical application of fitness landscape analysis. Alternatively, papers on fitness landscapes may appear alone in specific algorithm tracks, such as swarm intelligence or genetic programming.

Scope and Topics

The aim of this special session on fitness landscapes is to provide an opportunity to not only bring fitness landscape analysis researchers together at CEC 2018, but also to publish the most recent work in a dedicated track in the proceedings. In addition, the special session should be of interest to researchers and practitioners interested in practically applying fitness landscape analysis techniques to better understand problems and algorithm behaviour.

For this special session on fitness landscapes we invite researchers to submit unpublished work specifically focusing on the practice of fitness landscape analysis. Topics of interest include, but are not limited to:

  • Analysis of algorithm performance in relation to fitness landscape characteristics
  • Practical techniques for characterising the features of combinatorial problems with large search spaces or approximating the features of continuous search spaces
  • Practical measures for characterising dynamic landscapes
  • Practical analysis of the fitness landscapes of constrained optimisation problems
  • Characterisation of multiobjective optimisation problems
  • Online fitness landscape analysis for the characterisation of problems during search
  • Analysis of benchmark problem suites using fitness landscape techniques
  • Generation of new benchmark problems with particular fitness landscape characteristics
  • Analysis of the fitness landscapes of specific classes of problems or real-world optimisation problem instances to provide insight into algorithm behaviour or to highlight challenges in the practical application of fitness landscape analysis

 

CEC-28 Special Session on Differential Evolution: Past, Present and Future

Organized by Kai Qin (kqin@swin.edu.au), Swagatam Das, Rammohan Mallipeddi, and Efrén Mezura Montes

Website: http://alexkaiqin.org/DE-WCCI2018.htm

Differential evolution (DE) emerged as a simple and powerful stochastic real-parameter optimizer more than two decades ago and has now developed into one of the most promising research areas in the field of evolutionary computation. The success of DE has been ubiquitously evidenced in various problem domains, e. g., continuous, combinatorial, mixed continuous-discrete, single-objective, multi-objective, constrained, large-scale, multimodal, dynamic and uncertain optimization problems. Furthermore, the remarkable efficacy of DE in real-world applications significantly boosts its popularity.

Over the past decades, numerous studies on DE have been carried out to improve the performance of DE, to give a theoretical explanation of the behavior of DE, to apply DE and its derivatives to solve various scientific and engineering problems, as demonstrated by a huge number of research publications on DE in the forms of monographs, edited volumes and archival articles. Consequently, DE related algorithms have frequently demonstrated superior performance in challenging tasks. It is worth noting that DE has always been one of the top performers in previous competitions held at the IEEE Congress on Evolutionary Computation. Nonetheless, the lack of systematic benchmarking of the DE related algorithms in different problem domains, the existence of many open problems in DE, and the emergence of new application areas call for an in-depth investigation of DE.

Scope and Topics

This special session aims at bringing together researchers and practitioners to review and re-analyze past achievements, to report and discuss latest advances, and to explore and propose future directions in this rapidly emerging research area. Authors are invited to submit their original and unpublished work in the areas including, but not limited to:

  • DE for continuous, discrete, mixed, single-objective, multi-objective, constrained, large-scale, multiple optima seeking (niching), dynamic and uncertain optimization
  • Review, comparison and analysis of DE in different problem domains
  • Experimental design and empirical analysis of DE
  • DE-variants for handling mixed-integer, discrete, and binary optimization problems
  • Study on initialization, reproduction and selection strategies in DE
  • Study on control parameters (e.g., scale factor, crossover rate, and population size) in DE
  • Self-adaptive and tuning-free DE
  • Parallel and distributed DE
  • Theoretical analysis and understanding of DE
  • Synergy of DE with neuro-fuzzy and machine learning techniques
  • DE for expensive optimization problems
  • Hybridization of DE with other optimization techniques
  • Interactive DE
  • Application of DE to real-world problems

 

CEC-29 Special Session on Brain Storm Optimization Algorithms

Organized by Shi Cheng (cheng@snnu.edu.cn), Junfeng Chen, Hui Lu, and Yuhui Shi

The Brain Storm Optimization (BSO) algorithm is a new kind of swarm intelligence algorithm, which is based on the collective behaviour of human being, that is, the brainstorming process. There are two major operations involved in BSO, i.e., convergent operation and divergent operation. A “good enough” optimum could be obtained through recursive solution divergence and convergence in the search space. The designed optimization algorithm will naturally have the capability of both convergence and divergence.

BSO possess two kinds of functionalities: capability learning and capacity developing. The divergent operation corresponds to the capability learning while the convergent operation corresponds to capacity developing. The capacity developing focuses on moving the algorithm’s search to the area(s) where higher potential solutions may exist while the capability learning focuses on its actual search towards new solution(s) from the current solution for single point based optimization algorithms and from the current population of solutions for population-based swarm intelligence algorithms. The capability learning and capacity developing recycle to move individuals towards better and better solutions. The BSO algorithm, therefore, can also be called as a developmental brain storm optimization algorithm.

The BSO algorithm can also be seen as a combination of swarm intelligence and data mining techniques. Every individual in the brain storm optimization algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscapes of the problem. The swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.

Scope and Topics

This special session aims at presenting the latest developments of BSO algorithm, as well as exchanging new ideas and discussing the future directions of developmental swarm intelligence. Original contributions that provide novel theories, frameworks, and applications to algorithms are very welcome for this Special session.

Potential topics include, but are not limited to:

  • Theoretical aspects of BSO algorithms
  • Analysis and control of BSO parameters
  • Parallelized and distributed realizations of BSO algorithms
  • BSO for multiple/many objective optimization
  • BSO for constrained optimization
  • BSO for discrete optimization
  • BSO for large-scale optimization
  • BSO algorithm with data mining techniques
  • BSO in uncertain environments
  • BSO for real-world applications

 

CEC-30 Special Session on Big Optimization

Organized by El-ghazali Talbi (el-ghazali.talbi@univ-lille1.fr) and Amir Nakib

Website: https://bigopti.jimdo.com/

A lot of modern engineering and scientific applications are concerned by big optimization problems in terms of number of variables, objectives, constraints, data, uncertainties and so on. The goal of this session is to come up with cutting-edge evolutionary and metaheuristic approaches to deal with big optimization problems such as: parallel design and implementation, decomposition methods, model-based optimization, surrogate-based optimization, cross-domain, exascale and ultra scale optimization, big data applications, optimization under uncertainties, mixed optimization.

Scope and Topics

The aim of this special session is to explore potential evolutionary algorithms and metaheuristics to solve big optimization problems. For this purpose, this special session focuses on, but is not limited to, the following areas:

  • Parallel evolutionary algorithms and metaheuristics for complex problems
  • Big optimization under uncertainties
  • Surrogate-assisted evolutionary algorithms
  • Exascale and ultra-scale optimization
  • Cross-domain optimization
  • Large scale continuous and combinatorial optimization
  • Large scale mixed optimization
  • Expensive optimization problems (engineering design, data mining, …)
  • Big data applications
  • Multi-disciplinary optimization
  • Complex multi-objective optimization
  • Distributed evolutionary algorithms
  • Co-evolutionary algorithms
  • Decomposition-based evolutionary algorithms

 

CEC-31 Special Session on Evolutionary Optimization for Non-Convex Machine Learning

Organized by Ke Tang (tangk3@sustc.edu.cn), Yang Yu, and Chao Qian

Sophisticated optimization problems lay in many machine learning tasks. These problems were commonly smartly relaxed as convex optimization problems. Although the relaxation allows an efficient optimization using mathematical programming methods, it often shifts the learning problem and loses some important properties (e.g., convex loss functions may sensitive to data noise). Evolutionary optimization provides a set of direct search tools that make it possible to solve non-convex optimization problems for machine learning. This special session intends to bring together researchers to report their latest progress and exchange experience in solving machine learning tasks better with evolutionary optimization methods.

Scope and Topics

The interest of this special session is on solving non-convex optimization problems in machine learning with the methodologies related to evolutionary optimization, such as evolutionary algorithms, swarm intelligence algorithms, cross-entropy methods, Bayesian optimization. The topics cover a broad range of machine learning tasks including (but not limited to):

  • Supervised, semi-supervised, and multi-label learning
  • Learning deep models
  • Representation learning, sparse learning, dimension extraction
  • Reinforcement learning
  • Multi-instance learning
  • Cost-sensitive and imbalanced learning
  • Unsupervised learning and clustering
  • Parameter tuning

 

CEC-32 Special Session on Theoretical Foundations of Bio-inspired Computation

Organized by Dogan Corus, Andrei Lissovoi, and Pietro S. Oliveto (P.Oliveto@sheffield.ac.uk)

Website: http://staffwww.dcs.shef.ac.uk/people/P.Oliveto/CIStheory.html

Bio-inspired search heuristics often turn out to be highly successful for optimization in practice. The theory of these randomized search heuristics explains the success or the failure of these methods in practical applications. Theoretical analyses lead to the understanding of which problems are optimized (or approximated) efficiently by a given algorithm and which are not.

The benefits of theoretical understanding for practitioners are threefold: aiding the algorithm design, guiding the choice of the best algorithm for the problem at hand, and determining the optimal parameter settings. The theory of evolutionary computation has grown rapidly in recent years. The primary aim of this special session is to bring together people working on theoretical aspects of bioinspired computation. The latest breakthroughs in the theory of bio-inspired computation will be reported and new directions will be set.

Scope and Topics

Potential authors are invited to submit papers describing original contributions to foundations of evolutionary computation. Although we are most interested in theoretical foundations, computational studies of a foundational nature are also welcome. The scope of this special session includes (but is not limited to) the following topics:

  • Theoretical foundations of bio-inspired heuristics
  • Exact and approximation runtime analysis
  • Black box complexity
  • Self-adaptation
  • Population dynamics
  • Fitness landscape and problem difficulty analysis
  • No free lunch theorems
  • Statistical approaches for understanding the behavior of bio-inspired heuristics
  • Computational studies of a foundational nature

All problem domains will be considered including:

  • Combinatorial and continuous optimization
  • Single-objective and multi-objective optimization
  • Constraint handling
  • Dynamic and stochastic optimization
  • Co-evolution and evolutionary learning

 

CEC-33 Special Session on Artificial Immune Systems: Algorithms, Simulation, Modelling & Theory

Organized by Guilherme P. Coelho, Zaineb Chelly, Grazziela Figueredo, and Mario F. Pavone (mpavone@dmi.unict.it)

The Immune System protects organisms against diseases, and over the years has been an important source of inspiration for the development of algorithms to be applied in a wide range of applications, such as learning, pattern recognition, optimisation and classification. Many of these algorithms are built on solid theoretical foundations, through understanding mathematical models and computational simulation of aspects of the immune system. The scope of this research area ranges from modelling to simulation of the immune system, to the development of novel engineering solutions to complex problems, and bridges several disciplines to provide new insights into immunology, computer science, mathematics and engineering

Scope and Topics

This special session aims to focus on the recent advances on Artificial Immune Systems (AIS) field, also offering new conceptual models for understanding the dynamics that underlie the immune system.

Topics of interest include, but are not limited to:

  • Computational & Mathematical modelling of the Immune System
  • Theoretical aspects of immune inspired algorithms
  • Novel algorithms and new immune operators
  • Benchmarking immune inspired algorithms against other techniques
  • Empirical and Theoretical investigations into performance and complexity of immune inspired algorithms
  • Hybridisation of immune inspired algorithms with other techniques
  • Immune inspired algorithms for Big Data
  • Systems & Synthetic Immunology

 

CEC-34 Special Session Associated with Competition on (Bound-) Constrained Single Objective Numerical Optimization

Organized by P. N. Suganthan (epnsugan@ntu.edu.sg), Mostafa Z Ali, Guohua Wu, and Rammohan Mallipeddi

Website: http://www.ntu.edu.sg/home/epnsugan/index_files/CEC2018/CEC2018.htm

This special session is used to receive conference papers submitted to the above competition. The goals of the competition and special session are to evaluate the current state of the art in single objective numerical optimization with bound and/or general constraints. Single objective numerical optimization is the most important class of problems. All new evolutionary and swarm algorithms are tested on single objective benchmark problems. In addition, these single objective benchmark problems can be transformed into dynamic, niching, composition, computationally expensive and many other classes of problems.

Scope and Topics

Authors are free to make use of any population based algorithm to solve either the bound constrained problems or the constrained problems. Each submission should address either the bound constrained benchmark problems or the constrained benchmark problems. Performance evaluation criterion and how to present the results are detailed in the associated technical reports available at the above web link.

CEC-35 Special Session on Mathematical and Algorithmic Frameworks for Simulation of Swarm Intelligence

Organized by Carsten Mueller (carsten.mueller@itg-research.net)

Website: http://www.ieoca.org

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. In SI, an individual has a simple structure and its function is single. These systems composed by many individuals show the phenomenon of emergence and address several difficult and complex real world problems that are impossible to be solved by only an individual. During the recent decades, SI methods have been successfully applied to cope with complex and time-consuming problems that are hard to be solved by traditional mathematical methods. SI is a topic of interest among researchers in various fields of science and engineering. Several popular SI paradigms, including ant colony optimization, and particle swarm optimization, have been successfully applied to handle various practical engineering problems.

A common framework is desirable for a number of reasons, including the following: deep understanding of the learning algorithms employed for different tasks of data mining and optimization in Computational Swarm Intelligence techniques, discovering the relationships between parameter values and the interactions between parts of the analyzed approaches in the context of optimization, and exploitation of swarm and collective approaches in practical problems.

Scope and Topics

We encourage submission of papers describing new concepts and strategies, and systems and tools providing practical implementations, including hardware and software aspects. In addition, we are looking for mathematical and algorithmic frameworks which will enable us to understand and analyze these algorithms and the self-adaptive mechanisms and learning aspects.

The topics are, but not limited to, the following:

  • Mathematical and algorithmic frameworks for simulation of Swarm Intelligence (e.g. Ant Colony Optimization, Particle Swarm Optimization, Multi-agent systems)
  • Simulation of collective behavior using Swarm Robots (e.g. Kilobot)

 

CEC-36 Special Session on Parallel and Distributed Evolutionary Computation in the Inter-Cloud Era

Organized by Yuji Sato (yuji@k.hosei.ac.jp), Noriyuki Fujimoto, and Hiroyuki Sato

Recent advances in cloud computing lead to a global infrastructure of “the Inter-cloud” (clouds of cloud systems) that can be utilized through the Internet to provide with virtually infinite IT resources such as virtual machines and storage units just by calling web-service APIs through the Internet. It is necessary to have enough resources and complexities in the environment for the individuals to “evolve”. Cloud systems may even offer tens of thousands of virtual machines, terabytes of memories and exabytes of storage capacity. Current trend toward many-core architecture increases the number of cores even more dramatically: we may have more than a million of cores to offer extremely massive parallelization.

Scope and Topics

In this special session, we will discuss parallel and distributed evolutionary computation in the cloud era such as implementation of massively parallel evolutionary algorithms employing cloud computing systems and services, parallel implementation of evolutionary algorithms on many-core architectures including GPUs, and we also welcome any types of parallel and distributed evolutionary computation on any unconventional types of computing environment in this special session including the following themes:

  • Implementation of parallel and distributed evolutionary computation in cloud computing systems and/or services
  • Implementation of massively parallel evolutionary computation on many-core architecture such as GPUs
  • Parallel and distributed evolutionary multi/many objective optimization
  • Large-scale multi-objective optimization in cloud computing systems
  • Parallel and distributed Swarm Intelligence
  • Parallel and distributed evolutionary machine learning techniques
  • Design and theory of scalable evolutionary algorithms
  • Development of parallel and distributed evolutionary computation framework in cloud computing systems
  • Applications of parallel and evolutionary computation techniques in cloud or other modern computing environment
  • Applications of EC and other bio-inspired paradigms to peer to peer systems, and distributed EC algorithms that use them
  • Peer-to-peer computing, volunteer computing and zero-cost distributed computing. Large scale autonomous systems, sneaky or parasite computing using the browser or other widely available infrastructure. Internet of Things or Everything (IoT or IoE)

 

CEC-37 Special Session on Diversity Preservation Mechanisms for Population-based Meta-heuristics

Organized by Carlos Segura (carlos.segura@cimat.mx), Eduardo Segredo, and Gara Miranda

During last years, a wide range of population-based meta-heuristics, such as evolutionary algorithms and swarm-based approaches, among others, have been proposed with the aim of dealing not only with benchmark optimization problems, but also with real-world applications belonging to a significant number of fields. Population-based approaches maintain a set of solutions with the aim of exploring the search space in an efficient way. Usually, a diverse set of solutions is maintained, meaning that several regions are explored simultaneously. However, one common problem of population-based meta-heuristics is that for some test cases they might exhibit a tendency to converge quickly towards some regions. One of the most frequent problems that these types of meta-heuristics have to deal with is premature convergence, which arises when every member of the population is located at a sub-optimal area of the decision space from where they cannot escape. A significant number of methods have been proposed in order to preserve the diversity in a set of solutions, e.g., mating-based approaches, disruptive operators, fitness sharing, crowding-based selection, and methods based on complex population structures, among others. Furthermore, some more recent proposals such as diversity-based multi-objective approaches and multi-objectivization, have gained popularity during last years in the case of promoting diversity by means of the application of multi-objective algorithms to single-objective problems. Additionally, some of the methods that are used in multi-objective optimization to preserve diversity in the objective space are similar to those used in single-objective optimization in the decision space, so advances in one of the fields guide the advances in the other one.

The increasing importance on diversity preservation mechanisms can be demonstrated in several ways. First, current best-known solutions for several problems such as the frequency assignment problem, graph coloring problem and graph partitioning problem, among others, have been obtained with schemes that incorporate mechanisms to explicitly control the diversity. Additionally, the amount of papers that deal with the control of diversity has dramatically increased in the last years. A search of the terms “diversity” and “population” in Scopus limited to areas like “Computer Science” and “Mathematics”, results in more than 2,400 different occurrences considering the last five years.

Scope and Topics

Due to this increasing importance, this special session aims to attract the most recent advances produced in the following topics, including but not limited to:

  • Restarting mechanisms
  • Variable population size approaches
  • Methods based on mating restrictions
  • Diversity-based operators
  • Niching and crowding mechanisms
  • Management of the diversity in multi-objective evolutionary algorithms in the decision and/or objective space
  • Multi-objective methods for promoting diversity in single-objective optimization
  • Methods based on complex population structures
  • Clustering techniques
  • Management of diversity in memetic algorithms
  • Diversity metrics in combinatorial and continuous spaces
  • Premature convergence detection
  • Real-world applications requiring the application of diversity preservation mechanisms for being successfully solved.

 

CEC-38 Special Session on Hybrid Cultural Algorithms: Beyond Classical Cultural Algorithms

Organized by Robert G. Reynolds (reynolds@cs.wayne.edu), Mostafa Ali, Carlos Coello Coello, Yuhui Shi, and Gary Yen

Cultural Algorithms are computational models of Cultural Evolution. As such they provide a framework within which experiences of problem solvers embedded in a social fabric influence the collective knowledge of that group, its Culture. Culture is viewed as a network of passive and active knowledge sources. These knowledge sources are able integrate this knowledge, either individually or collectively, into their structure using data mining and machine learning tools. This updated Cultural Knowledge then is used to direct the modifications to individuals and their plans in the population space. Cultural Algorithms are an ideal framework for problems that require large amounts of domain knowledge to direct the collective decisions of individuals in the population. As such Cultural Algorithms have been successfully applied to problems in complex hierarchical systems characterized by large and extensive data sets (big data), many domain constraints, multiple objectives, and multiple agents within a large and spatially distributed social network.

Cultural Algorithm can also provide a flexible framework for hybridization with other socially motivated technologies such as particle swarm optimization, differential evolution, ant colony optimization, and co-evolutionary approaches among others. These hybrid systems have required extension to Classical Cultural Algorithms such as multi-population and multi-belief spaces, novel approaches to using belief space knowledge to drive evolutionary search. This special session is designed to provide an overview of the diverse hybrid approaches that have been proposed beyond the classical Cultural Algorithm. Cultural Algorithm designers are invited to submit their latest extensions and share a glimpse of the future of Cultural Algorithms.

Scope and Topics

This special session will focus on all aspects of Cultural Algorithms theory and application. Topics of interest may cover, but are not limited to the following:

  • Big data and analytics with cultural algorithms
  • Social intelligence in networks
  • Brainstorming in cultural systems
  • Bio-informatics applications
  • Multi-cultural systems and subcultures
  • Multi-objective optimization
  • Many objective optimization
  • Multi-population, multi-agent systems
  • Ecosystem modelling and virtual world applications
  • Hybrid system learning systems
  • Distributed computing
  • Social intelligence in games and auctions
  • Cloud computing applications
  • Constrained optimization
  • Real-world applications
  • Crowd sourcing
  • Hybrid agent populations: GA, GP, neural, and fuzzy agents
  • Education
  • Deep learning in cultural algorithms
  • Hierarchical swarms

 

CEC-39 Special Session on Deep Neuroevolution

Organized by Oliver Kramer (oliver.kramer@uni-oldenburg.de) and Chuan-Kang Ting

Website: https://www.deepneuroevolution.com

The success of deep learning is impressive. While achieving human-like or even superhuman performance in various domains, the optimal choices of neural network structures, architectures, layer types, and hyperparameters are often difficult to take, and up to now mostly based on human expertise and manual tuning. The automatic tuning of deep learning networks is of high interest, making the task an excellent research area for evolutionary computation. Evolutionary algorithms allow an efficient search in the design space of deep neural architectures. They comprise powerful tools for search in combinatorial spaces of neural layers and in continuous and discrete spaces of hyperparameters. The availability of great computational resources, often based on massive GPU parallelization today, allows the evolution of deep learning architectures in reasonable time. However, there are many open research issues to investigate, e.g., the classic questions like population models and operator types, as well as the novel topics arising from methodological characteristics of specialized deep learning models.

Scope and Topics

The aim of this special session is to promote research and reflect the most recent advances in deep neuroevolution. Topics of interest include, but are not limited to:

  • Evolutionary search in the design space of deep learning networks
  • Evolutionary search for deep learning hyperparameters
  • Evolution of novel layer types, their combination, and parameterization
  • Highly parallel frameworks or toolkits to support deep neuroevolution
  • Representation, genotype-phenotype mapping for deep networks
  • Development and analysis of new population types and genetic operators
  • Methods for the analysis of new evolved designs
  • Multi-objective evolutionary search for deep neural networks
  • Deep neuroevolution in special applications (e.g. medical image analysis, image and speech recognition)
  • Deep neuroevolution for special network types (e.g. convolutional neural networks, capsule networks, and deep autoencoders)
  • Related topics in the intersection between evolutionary computation and deep learning

 

CEC-40 Special Session on Evolutionary Optimization Methods in Energy Internet

Organized by Rui Wang (ruiwangnudt@gmail.com), Guohua Wu and Tao Zhang

Website: http://ruiwangnudt.gotoip3.com/EAforEI2018WCCI.mht

Due to the rapid industrialization and the scarcity of conventional energy resources such as coal and natural gas, it has become increasingly urgent to find effective and efficient ways for energy use. The “Energy Internet (EI)” is a peer to peer energy exchange and sharing network which effectively integrates different energy sources together, including both conventional energy resources and renewable energy sources like solar and wind, and has become a promising solution. However, there are various optimization issues existed in EI. For example, the optimal structure design of the EI, the optimal control and management of energy exchange, and the optimal scheduling of energy flow among different nodes. Often such problems are not easy to solve by traditional mathematical programming methods, which thus, requires advanced methods, e.g., evolutionary algorithms. Moreover, hybrid renewable energy systems are often used in an EI. The design of HRES requires to optimize multiple objectives (such as the lifetime system cost, carbon emissions, and the system reliability), and so is effectively a multi-objective optimization problem which calls for effective evolutionary multi-objective algorithms. Overall, the need for researchers from both optimization side and energy side to develop more effective and efficient methods to tackle issues arise in Energy Internet Systems has become apparent.

Scope and Topics

The main aim of this special session is to bring together both experts and new-comers from either academia or industry to discuss new and existing optimization issues in an EI, in particular, to cross-fertilizate between academic research and industry applications, and to stimulate further engagement with the user community.

Full papers are invited on recent advances in the development of EIs, new horizons, i.e., using multi-criteria decision making methods, for EI design and/or management. In addition, we are interested in various studies discussing optimization issues in EIs or related real-world applications. You are invited to develop new evolutionary algorithms for the topics below, but are not limited to:

  • The optimal structure design of the EI
  • The optimal control and management of energy exchange
  • The optimal scheduling of energy flow among different nodes
  • Large decision variables based EI optimal design or/and management
  • Multi-objective optimal design of HRES via evolutionary computation
  • HRES optimal design under dynamic environments
  • HRES optimal design under uncertain environments
  • Robust optimal design of HRES
  • Optimization methods for other energy related real-world problems
  • Other studies related EI optimization

 

CEC-41  Special Session on Large-Scale Global OptimizationOrganized by Daniel Molina (dmolina@decsai.ugr.es) and Antonio LaTorre

Website: http://www.tflsgo.org/special_sessions/cec2018.html

In the past two decades, many nature-inspired optimization algorithms have been developed and successfully applied for solving a wide range of optimization problems, including Simulated Annealing (SA), Evolutionary Algorithms (EAs), Differential Evolution (DE), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Estimation of Distribution Algorithms (EDA), etc. Although these techniques have shown excellent search capabilities when applied to small or medium sized problems, they still encounter serious challenges when applied to large scale problems, i.e., problems with several hundreds to thousands of variables. The reasons appear to be two-fold. Firstly, the complexity of a problem usually increases with the number of decision variables, constraints, or objectives (for multi-objective optimization problems). Problems with this high level of complexity may prevent a previously successful search strategy from locating the optimal solutions. Secondly, as the size of the solution space of the problem grows exponentially with the increasing number of decision variables, there is an urgent need to develop more effective and efficient search strategies to better explore this vast solution space with only limited computational budgets.

In recent years, research on scaling up EAs to large-scale problems has attracted significant attention, including both theoretical and practical studies. Existing work on tackling the scalability issue is getting more and more attention in the last few years. This special session is devoted to highlight the recent advances in EAs for handling large-scale global optimization (LSGO) problems, involving single objective or multiple objectives, unconstrained or constrained, binary/discrete or real, or mixed decision variables.

Scope and Topics

We encourage interested researchers to submit their original and unpublished work on:

  • Theoretical and experimental analysis on the scalability of EAs;
  • Novel approaches and algorithms for scaling up EAs to large-scale optimization problems;
  • Applications of EAs to real-world large-scale optimization problems;
  • Novel test suites that help researches to understand large-scale optimization problems characteristics.

There is a LSGO competition which is being organized along with the special session. Nonetheless, participating in the competition is not mandatory and any work on the LSGO field is welcome.

 

CEC-42  Special Session on Dynamic Multi-objective OptimizationOrganized by Kalyanmoy Deb and Mardé Helbig (mhelbig@cs.up.ac.za)

Most real-world optimization problems have more than one objective, with at least two objectives that are in conflict with one another. The conflicting objectives of the optimization problem lead to an optimization problem where a single solution does not exist, as is the case with single-objective optimization problems (SOOPs).Instead of a single solution, a set of optimal trade-off solutions exists, referred to as the Pareto-optimal front (POF), Pareto front or Pareto frontier. This kind of optimization problems are referred to as multi-objective optimization problems (MOOPs).

In many real-world situations the environment does not remain static, but is dynamic and changes over time. However, in recent years most research was focused on either static MOOPs or dynamic SOOPs. When solving dynamic multi-objective optimization problems (DMOOPs) an algorithm has to track the changing POF over time, while finding solutions as close as possible to the true POF and maintaining a diverse set of solutions. Some of the major challenges in the field of dynamic multi-objective optimization (DMOO) are a lack of a standard set of benchmark functions, a lack of standard performance measures, issues with performance measures currently being used for DMOO, a lack of a comprehensive analysis of existing algorithms applied to DMOO, a lack of approaches to incorporate the decision maker’s preferences into the search and a lack of visualization techniques for DMOO.

Scope and Topics

This special session aims to highlight the latest developments in dynamic multi-objective optimization (DMOO) in order to bring together researchers from both academia and industry to address the above mentioned challenges and to explore future research directions for the field of DMOO. We invite authors to submit original and unpublished work on DMOO. Topics of interest include, but are not limited to:

  • DMOO benchmark functions
  • Performance measures for DMOO
  • Constrained DMOO
  • New DMOO algorithms
  • Comparative studies of DMOO algorithms
  • Theoretical aspects of DMOO algorithms
  • Approaches to handle outlier solutions
  • Real-world applications of DMOO algorithms
  • Decision making for DMOO
  • Visualization approaches for DMOO

 

CEC-43  Special Session on Advances in Decomposition-based Evolutionary Multi-objective OptimizationOrganized by Saúl Zapotecas (szapotecas@correo.cua.uam.mx), Bilel Derbel, and Qingfu Zhang

The purpose of this special session is to promote the design, study, and validation of generic approaches for solving multi-objective optimization problems based on the concept of decomposition. Decomposition-based Evolutionary Multi-objective Optimization (DEMO) encompasses any technique, concept or framework that takes inspiration from the “divide and conquer” paradigm, by essentially breaking a multi-objective optimization problem into several sub-problems for which solutions for the original global problem are computed and aggregated in a cooperative manner. This simple idea, which is rather standard in computer science and information systems, allows to open up new exciting research perspectives and challenges both at the fundamental level of our understanding of multi-objective problems, and in terms of designing and implementing new efficient algorithms for solving them. Generally speaking, the special session will focus on stochastic evolutionary approaches for which decomposition is performed with respect to the objective space, typically by means of scalarizing functions like in the MOEA/D framework. We, however, encourage contributions reporting advances with respect to other decomposition techniques operating in the decision space as done in the co-called cone-speration methods; or other hybrid approaches taking inspiration from operations research and mathematical programming. In fact, many different DMOEAs variants have been proposed, studied and applied to various application domains in recent years. However, DMOEAs are still in their very early infancy, since only few basic design principles have been established compared to the huge body of literature dedicated to other well-established approaches (e.g. Pareto ranking, indicatorbased techniques, etc), and relatively few research forums have been dedicated to the study of DEMO approaches and their unification. The main goal of the proposed session is to encourage research studies that systematically investigate the critical issues in DMOEAs at the aim of understanding their key ingredients and their main dynamics, as well a to develop solid and generic principles for designing them. The long term goal is to contribute to the emergence of a general and unified methodology for the design, the tuning and the performance assessment of DMOEAs.

Scope and Topics

The topics of interests include (but are not limited to) the following issues:

  • Analysis of algorithmic components and performance assessment of DEMO approaches
  • Experimental and theoretical investigations on the accuracy of the underlying decomposition strategies, e.g. scalarizing functions techniques, multiple reference points, variable grouping, etc.
  • Adaptive, self-adaptive, and tuning aspects for the parameter setting and configuration of DEMO approaches
  • Design and analysis of new DEMO approaches dedicated to specific combinatorial, constrained and/or continuous domains
  • Effective hybridization of single-objective solvers with DEMO approaches, i.e., plug-and-play algorithms based on traditional single objective evolutionary algorithms and metaheuristics, such as: Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Covariance Matrix Evolution Strategy (CMA-ES), Scatter Search (SS), etc.
  • Adaptation and analysis of DEMO approaches in the context of large scale and many objective problem solving
  • Application of DEMO for real-world problem solving
  • Design and implementation of DEMO approaches in massively parallel and large scale distributed environment (e.g., GPUs, Clusters, Grids, etc.)
  • Software tools for the design implementation and performance assessment of DEMO approaches

CEC-44 Special Session on Evolutionary Methods and Machine Learning in Software Engineering, Testing and SE Repositories

Organized by Daniel Rodríguez (daniel.rodriguezg@uah.es), Jose A. Lozano, Francisco Chicano, and Francisco Palomo Lozano

This special session aims to bring together both theoretical developments and applications of Computational Intelligence to software engineering (SE), i.e., the management, design, development, operation, maintenance, and testing of software. All bio-inspired computational paradigms and machine learning techniques are welcome, such as Genetic and Evolutionary Computation, including Multi-Objective Approaches, Fuzzy Logic, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems, Swarm Intelligence, and others,
including machine learning techniques.

Currently, an increasing number of researchers from the SE discipline are focusing on applying computational intelligence techniques such as meta-heuristics (known as Search based software engineering, SBSE), data mining or statistics to their research. Problems such as planning and decision making in software engineering, arrangements of modules, finding patterns of defective modules, cost and effort estimation, testing and test case generation,  ebugging and fault localization, knowledge extraction, etc. can be reformulated or addressed using a set of techniques which includes searching and optimization techniques, data mining and machine learning,
simulation, process mining, etc. These techniques, already used extensively in other areas, are incrementally being applied in software engineering.

There are a large number of decisions during the development and maintenance of any software system. Evolutionary methods and data mining can help with the decision making process based on the information available (e.g., estimation and planning of projects) or with the generation of artifacts (e.g., test case generation). Furthermore, modern development environments (IDEs, Issue Tracking Systems and Configuration Management Systems) allow us to collect large amount of data during the executing of a project for real-time decisions as well as application repositories (AppStore, Google Play) containing huge amount of valuable information that can be exploited.

Scope and Topics

The aim of this special session is to provide a forum for the presentation of the latest data, results, and future research directions on Evolutionary Methods and Machine Learning in Software Engineering, Testing and SE Repositories. The special session invites submissions in any of the following areas:

  • Search-based Software Engineering
  • Requirements engineering
  • Automated design and development of software
  • Genetic improvement of software
  • Software maintenance and self-repair
  • Software effort estimation and fault prediction
  • Software reliability, testing and security with data-mining or meta-heuristic techniques
  • Project management, planning and scheduling
  • Studies, applications and tools to extract information from software repositories
  • Dealing with data problems in software repositories (noise, imbalance, outliers, etc.) when
    applying ML or meta-heuristics
  • Process mining
  • Mining mobile application repositories (AppStore and Google Play)
  • Tools based on evolutionary or ML methods in SE
  • Real world applications of the above