Introduction to Evolutionary Algorithms (Decision Engineering)

Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline. Front Matter....Pages i-xvi Front Matter....Pages 1-1 Introduction....Pages 3-10 Simple Evolutionary Algorithms....Pages 11-38 Advanced Evolutionary Algorithms....Pages 39-132 Front Matter....Pages 133-133 Constrained Optimization....Pages 135-164 Multimodal Optimization....Pages 165-191 Multiobjective Optimization....Pages 193-262 Combinatorial Optimization....Pages 263-324 Front Matter....Pages 325-325 Swarm Intelligence....Pages 327-354 Artificial Immune Systems....Pages 355-379 Genetic Programming....Pages 381-401 Back Matter....Pages 403-418