Accepted Tutorials

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

Tutorials accepted for IEEE WCCI 2022.

Conference	Title
CEC	Towards Better Explainable AI Through Genetic Programming
CEC	Evolutionary Machine Learning for Combinatorial Optimisation
CEC	Evolutionary Algorithms and Hyper-Heuristics
CEC	How to Evaluate the Outcome of Multi-Objective Optimisation Algorithms
CEC	Pareto Optimization for Subset Selection: Theories and Practical Algorithms
CEC	Evolutionary Continuous Dynamic Optimization
CEC	Statistical Analyses for Multi-objective Stochastic Optimization Algorithms
CEC	Introduction into Matrix Adaptation Evolution Strategies
CEC	Network analysis and evolutionary dynamics on graphs
CEC	Evolutionary Many-Objective Optimization
CEC	Evolutionary Feature Reduction for Machine Learning
CEC	Benchmarking and analyzing iterative optimization heuristics with IOHprofiler
CEC	Principle and Applications of Semantic GP
CEC	How to Compare Evolutionary Multi-Objective Optimization Algorithms: Parameter Specifications, Indicators and Test Problems
CEC	Learn to Optimize
CEC	Differential Evolution with Ensembles, Adaptations and Topologies
CEC	Landscape Analysis of Optimisation Problems and Algorithms
CEC	Constraint Handling in Multiobjective Optimization
CEC	External Archivers for Multi-objective Evolutionary Algorithms
CEC	Tutorial on Modern Linkage Learning Techniques in Combinatorial Optimization
FUZZ-IEEE	Fuzzy Networks: Analysis and Design
FUZZ-IEEE	Graded logic aggregation and its applications in decision engineering
FUZZ-IEEE	FuzzyR: Fuzzy Logic Toolkit for R
IJCNN	Introduction to self-supervised learning and its applications
IJCNN	Introduction to Hardware-Aware Neural Architecture Search
IJCNN	Privacy-preserving machine and deep learning with homomorphicencryption: an introduction
IJCNN	Explainable AI (XAI) for Computer Vision – A Review of Existing Methods and a New Method to Extract a Symbolic Model from a CNN model
IJCNN	Neural Network Self-learning: Consequence-driven Systems Theory
IJCNN	Brain-inspired spiking neural networks for deep learning and knowledge representation: Methods, Systems, Applications
IJCNN	Randomization Based Deep and Shallow Learning Methods for Classification and Forecasting
IJCNN	Ethical Challenges in Computational Intelligence Research
IJCNN	Experience Replay for Deep Reinforcement Learning