Wiley Evolutionary Algorithms (Computer Engineering: Metaheuristics, 9)

Product Description Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods.In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms.Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning. Review In general, Petrowski and Ben-Hamid display an in-depth understanding of severaloptimization classes and their corresponding evolutionary algorithms, along with animpressive ability to explain, illustrate, motivate, classify and codify. Although nobodycan “do it all” in a field as deep and wide as evolutionary computation, they have chosena pertinent subset and done a fine job with it. My own copy of “Evolutionary Algorithms” became an instant go-to reference as I prepare for another semester of teaching.( Genetic Programming and Evolvable Machines, December 2018)    About the Author Alain PÉTROWSKI is Associate Professor in the Department of Networks and Mobile Multimedia Services at the Telecom-SudParis, Institut Mines-Télécom, Paris-Saclay University, France. His main research interests are related to optimization, metaheuristics and machine learning.