Accepted Tutorials

We are glad to announce the tutorials that have been accepted for WCCI 2022.

 
Tutorials accepted for IEEE WCCI 2022.
ConferenceTitle
CECTowards Better Explainable AI Through Genetic Programming
CECEvolutionary Machine Learning for Combinatorial Optimisation
CECEvolutionary Algorithms and Hyper-Heuristics
CECHow to Evaluate the Outcome of Multi-Objective Optimisation Algorithms
CECPareto Optimization for Subset Selection: Theories and Practical Algorithms
CECEvolutionary Continuous Dynamic Optimization
CECStatistical Analyses for Multi-objective Stochastic Optimization Algorithms
CECIntroduction into Matrix Adaptation Evolution Strategies
CECNetwork analysis and evolutionary dynamics on graphs
CECEvolutionary Many-Objective Optimization
CECEvolutionary Feature Reduction for Machine Learning
CECBenchmarking and analyzing iterative optimization heuristics with IOHprofiler
CECPrinciple and Applications of Semantic GP
CECHow to Compare Evolutionary Multi-Objective Optimization Algorithms: Parameter Specifications, Indicators and Test Problems
CECLearn to Optimize
CECDifferential Evolution with Ensembles, Adaptations and Topologies
CECLandscape Analysis of Optimisation Problems and Algorithms
CECConstraint Handling in Multiobjective Optimization
CECExternal Archivers for Multi-objective Evolutionary Algorithms
CECTutorial on Modern Linkage Learning Techniques in Combinatorial Optimization
FUZZ-IEEEFuzzy Networks: Analysis and Design
FUZZ-IEEEGraded logic aggregation and its applications in decision engineering
FUZZ-IEEEFuzzyR: Fuzzy Logic Toolkit for R
IJCNNIntroduction to self-supervised learning and its applications
IJCNNIntroduction to Hardware-Aware Neural Architecture Search
IJCNNPrivacy-preserving machine and deep learning with homomorphicencryption: an introduction
IJCNNExplainable AI (XAI) for Computer Vision – A Review of Existing Methods and a New Method to Extract a Symbolic Model from a CNN model
IJCNNNeural Network Self-learning: Consequence-driven Systems Theory
IJCNNBrain-inspired spiking neural networks for deep learning and knowledge representation: Methods, Systems, Applications
IJCNNRandomization Based Deep and Shallow Learning Methods for Classification and Forecasting
IJCNNEthical Challenges in Computational Intelligence Research
IJCNNExperience Replay for Deep Reinforcement Learning