CS361 (Software Engineering Program) Artificial Intelligence II - Applied Machine Learning Lecture 3 Evolutionary Computation: Differential Evolution as an Optimization Method Amr S. Ghoneim (Assistant Professor, Computer Science Dept.) Helwan University Fall 2019 Lecture is based on its counterparts in the following courses (and the following resources): o Evolutionary Computing, University of Vaasa (Finland), Electrical & Energy Eng. o Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Journal of Global Optimization 11: 341–359, 1997. o Data Clustering Method based on Mixed Similarity Measures, (ICORES 2017), 192-199 o Differential Evolution, University of Colorado (USA), Colorado Springs. Machine Learning? {Artificial Intelligence} Machine Learning Map 2 Recap: Evolutionary Computation Genetic Algorithms 3 Evolutionary Computation (EC) o Evolutionary Computing (or Evolutionary Algorithms “EA”) is a field of science and engineering that tries to apply some phenomena that appears in the nature to the optimization. o Most notable is the adaptation of Charles Darwin’s evolution theory in order to solve difficult search and optimization tasks. o The method is universally applicable, since it has been successfully applied to almost all thinkable search & optimization problems in engineering, science & people’s everyday life. General Scheme of Genetic Algorithms Pseudocode for a Typical Genetic Algorithm (GA) Example: The 8 Queens Problem .. ➢ Place 8 queens on an 8x8 chessboard in such a way that they cannot check each other. The 8 Queens Problem: Representation .. Phenotype: A board’s configuration .. Genotype: A permutation of the numbers 1 : 8 Obvious mapping 1 3 5 2 6 4 7 8 The 8 Queens Problem: Fitness Function (Evaluation) .. o Penalty of one queen: the number of queens she can check. o Penalty of a configuration: the sum of the penalties of all queens. o Note: penalty is to be m