Evolutionary Computation

Course Objectives :

1. To understand Evolutionary Algorithms in the context of meta-heuristics
2. To understand Evolutionary Algorithm’s important parametric components
3. To learn to formulate a given problem as an optimization problem and apply EAs
4. To understand and appreciate the state-of-the-art research in EC

Course Outcomes (CO)

1. CO 1 - To Formulate a given problem amenable for evolutionary optimization/search
2. CO 2 - To analyse and apply appropriate evolutionary algorithms for a given problem
3. CO 3 - To Analyse the state-of-the-art evolutionary computation research literature and apply them for solving dynamic problems
4. CO 4 - To Design suitable evolutionary algorithms for a real world application

UNIT-I

Introduction to Evolutionary Computation – Evolutionary Algorithms (Genetic Algorithms, Genetic Programming, Differential Evolution, Evolution Strategies, Covariance Matrix Adaptation etc.) – Different Components of Evolutionary Algorithms.

UNIT-II

Fitness Landscapes – Adaptive Parameter Control and Tuning – Constraint Handling – Niching and Fitness Sharing – Memetic Algorithms – Ensemble Evolutionary Algorithms

UNIT-III

Multi-Objective Optimization – Hyper-Heuristics – Special Forms of Evolution (Co-evolution and Speciation) –Theoretical Analysis of Evolutionary Algorithms – Interactive Evolutionary Algorithms

UNIT - IV

Evolutionary Machine Learning – Surrogate Assisted Optimization –Neuro Evolution-Open Ended Evolution. Applications of Evolutionary Algorithms

Textbook(s):

1. E. Eiben and J. E. Smith, “An Introduction to Evolutionary Computing”, Natural Computing Series, Springer, 2nd Edition, 2015.

References:

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