Course

Evolutionary Algorithms (Genetic Algorithms, Genetic Programming, Differential Evolution, Evolution Strategies, Covariance Matrix Adaptation etc.) – Different Components of Evolutionary Algorithms.
Fitness Landscapes – Adaptive Parameter Control and Tuning – Constraint Handling – Niching and Fitness Sharing – Memetic Algorithms – Ensemble Evolutionary Algorithms – Hybridization with other techniques – Multi-Objective Optimization – Hyper-Heuristics – Special Forms of Evolution (Co-evolution and Speciation) – Experimental (statistical) Methods for the analysis of Evolutionary Algorithms – Theoretical Analysis of Evolutionary Algorithms – Interactive Evolutionary Algorithms – Experiment design and analysis involving Evolutionary Algorithms
Evolutionary Machine Learning – Surrogate Assisted Optimization – NeuroEvolution – Quality Diversity Algorithms – Open Ended Evolution. Applications of Evolutionary Algorithms.

1. A. E. Eiben and J. E. Smith, “An Introduction to Evolutionary Computing”, Natural Computing Series, Springer, 2nd Edition, 2015.
2. Eyal Wirsansky, “Hands-On Genetic Algorithms with Python: Applying Genetic Algorithms to Solve Real-World Deep Learning and Artificial Intelligence Problems”, Packt Publishing, 2020.
3. Iaroslav Omelianenko, “Hands-on Neuroevolution with Python: Build High Performing Artificial Neural Network Architectures using Neuroevolution-based Algorithm”, Packt Publishing, 2019.
4. Slim Bechikh, Rituparna Datta and Abhishek Gupta (Eds.), “Recent Advances in Evolutionary Multi-objective Optimization”, Adaptation, Learning, and
   Optimization Book – 20, Springer, 2017.
5. Nelishia Pillay and Rong Qu, “Hyper-Heuristics: Theory and Applications”, Springer, 2018.
6. Hitoshi Iba, “Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-Evolution and Gene Regulatory Networks”, Springer, 2018.