Evolutionary Algorithms

Alain Petrowski

ISBN 10: 1848218044 / ISBN 13: 9781848218048

Published by ISTE Ltd and John Wiley & Sons Inc, 2017

About this Item

Evolutionary Algorithms are metaheuristics that use the metaphor of the Darwinian or Lamarckian evolution theories to find efficient solutions for hard optimization problems or machine learning. This book will present thoroughly a state-of-the-art of leading evolutionary algorithms with comparisons between them on test problems. Num Pages: 200 pages. BIC Classification: UMB. Category: (P) Professional & Vocational. Weight in Grams: 666. . 2017. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Seller Inventory # V9781848218048

Bibliographic Details

• Title: Evolutionary Algorithms
• Publisher: ISTE Ltd and John Wiley & Sons Inc
• Publication Date: 2017
• Language: English
• ISBN 10: 1848218044
• ISBN 13: 9781848218048
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

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.

Sana BEN-HAMIDA is Associate Professor at the Paris Ouest University and Associate Researcher at the Computer Science Laboratory of the Paris Dauphine University in France. Her main research interests include evolutionary computation, machine learning and related applications.