March 8, 2018


Times 1 2 3 4 5 6 7 8 9 10 11
8.00 – 10.00 Session 1 CEC1_01 CEC2-01 CEC3_01 CEC4_01 FUZZ5_01 (Part 1) FUZZ6_01 (Part 1) HYB7_01 Part 1 IJCNN1_01 (Part 1) IJCNN1_02 (Part 1) IJCNN3_02 (Part 1) IJCNN4_01 (Part 1)
10.00-10.15 Coffee
10:15 – 12.15 Session 2 CEC1-02 CEC2-02 CEC3_02 CEC4_02 FUZZ5_01 (Part 2) FUZZ6_01 (Part 2) HYB7_01 (part 2) IJCNN1_01 (Part 2) IJCNN1_02 (Part 2) IJCNN3_02 (Part 2) IJCNN4_01 (Part 2)
12.15- 13:00 Lunch
13:00 – 15:00 Session 3 CEC1-03 CEC2_03 (Part 1) CEC3_03 CEC4_03 (Part 1) FUZZ5_02 FUZZ6_02 (Part 1) HYB7_02 IJCNN1_03 IJCNN1_02 (Part 3) IJCNN3_01 IJCNN4_02
15:00 – 15.15 Coffee
15:15 – 17:15 Session 4 CEC1-04 CEC2_03 (Part 2) CEC3_04 CEC4_03 (Part 2) FUZZ5_03 FUZZ6_02 (Part 2) HYB7_03 IJCNN1_04 IJCNN1_02 (Part 4) IJCNN3_03 IJCNN4_03 (Part 1)
17:15 – 19:15 Session 5 CEC1-05 CEC2-04 CEC3_05 CEC4_05 FUZZ5_04 IJCNN3_04 HYB7_04 IJCNN1_05 IJCNN2_01 IJCNN3_05 IJCNN4_03 (Part 2)

CEC1_01 Co-evolutionary games
CEC1_02 Differential Evolution with Ensembles, Adaptations and Topologies
CEC1_03 Multi-concept Optimization
CEC1_04 Evolutionary Bilevel Optimization
CEC1_05 Evolutionary Computation for Dynamic Optimization Problems
CEC2_01 Applying Stopping Criteria in Evolutionary Multi-Objective Optimization
CEC2_02 Dynamic Multi-objective Optimization: Challenges, Applications and Future Directions
CEC2_03 Evolutionary Many-Objective Optimization
CEC2_04 Pareto Optimization for Subset Selection: Theory and Applications in Machine Learning
CEC3_01 Evolutionary Large-Scale Global Optimization: An Introduction
CEC3_02 Representation in Evolutionary Computation
CEC3_03 Parallel and distributed evolutionary algorithms
CEC3_04 Evolutionary Algorithms and Hyperheuristics
CEC3_05 Parallelization of Evolutionary Algorithms: MapReduce and Spark
CEC4_01 Gentle Introduction to the Time Complexity Analysis of Evolutionary Algorithms
CEC4_02 Constraint-Handling in Nature-Inspired Optimization
CEC4_03 Machine Learning on Evolutionary Computation
CEC4_04 Evolutionary Algorithms, Swarm Dynamics, and Complex Networks: Recent Advances and Progress

IJCNN1_01 Deep Learning for Sequences
IJCNN1_02 Deep Recurrent Neural Networks: Training and Applications in the Modeling and Control of Nonlinear Systems, Signal Processing and Robotics.
IJCNN1_03 Prediction, Interaction, and User Behaviour
IJCNN1_04 Entropic Evaluation of Classification. A hands-on, get-dirty introduction
IJCNN1_05 Machine Learning for Spark Streaming with StreamDM
IJCNN2_01 Learning class imbalanced data streams
IJCNN3_01 Adaptive Resonance Theory in Social Media Clustering with Applications
IJCNN3_02 Artificial Intelligence in Business (Changed to: Reinforcement Learning: Principles, Algorithms and Applications)
IJCNN3_03 Non-Iterative Learning Methods for Classification and Forecasting
IJCNN3_04 Graph based and Topological Unsupervised Machine Learning
IJCNN3_05 Methods and Resources for Texture Classification
IJCNN4_01 Tutorial on dynamic classifier selection: recent advances and perspectives
IJCNN4_02 Deep, Transfer and Emergent Reinforcement Learning Techniques for Intelligent Agents
IJCNN4_03 Neurosymbolic Learning and Reasoning with Constraints

FUZZ5_01 Type-2 Fuzzy Sets and Systems
FUZZ5_02 Fuzzy Systems in Medicine and Healthcare
FUZZ5_03 Support Fuzzy Machines: From Kernels on Fuzzy Sets to Machine Learning Applications
FUZZ5_04 Fuzzy Sets, Computer Science and (Fuzzy) Algorithms
FUZZ6_01 Fuzzy Logic and Machine Learning
FUZZ6_02 Uncertainty Modeling in Learning from Big Data

HYB7_01 Computational Intelligence for Data Science and Big Data
HYB7_02 Empirical Approach: How to get Fast, Interpretable Deep Learning
HYB7_03 Interactive Adaptive Learning
HYB7_04 Ranking: from social psychology to algorithms and back