Applied Evolutionary Algorithms in Java

Robert Ghanea-Hercock

ISBN 10: 0387955682 / ISBN 13: 9780387955681

Published by Springer, 2003

About this Item

Leichte Kratzer / Abnutzungen / Druckstellen; Vergilbt / ausgeblichen; Hat Beilage / CD. Genetic algorithms provide a powerful range of methods for solving complex engineering search and optimization algorithms. Their power can also lead to difficulty for new researchers and students who wish to apply such evolution-based methods. "Applied Evolutionary Algorithms in Java" offers a practical, hands-on guide to applying such algorithms to engineering and scientific problems. The concepts are illustrated through clear examples, ranging from simple to more complex problems domains; all based on real-world industrial problems. Examples are taken from image processing, fuzzy-logic control systems, mobile robots, and telecommunication network optimization problems. The Java-based toolkit provides an easy-to-use and essential visual interface, with integrated graphing and analysis tools. Topics and features: *inclusion of a complete Java toolkit for exploring evolutionary algorithms *strong use of visualization techniques, to increase understanding *coverage of all major evolutionary algorithms in common usage *broad range of industrially based example applications *includes examples and an appendix based on fuzzy logic This book is intended for students, researchers, and professionals interested in using evolutionary algorithms in their work. No mathematics beyond basic algebra and Cartesian graphs methods are required, as the aim is to encourage applying the Java toolkit to develop the power of these techniques.

Seller Inventory # 4fc60c7b-b478-44e2-afd1-b47757a27f71

Bibliographic Details

• Title: Applied Evolutionary Algorithms in Java
• Publisher: Springer
• Publication Date: 2003
• Language: English
• ISBN 10: 0387955682
• ISBN 13: 9780387955681
• Binding: Hardcover
• Condition: Fine
• Seller catalogs:
  • Mathematics

About this title

Synopsis

Genetic algorithms provide a powerful range of methods for solving complex engineering search and optimization algorithms. Their power can also lead to difficulty for new researchers and students who wish to apply such evolution-based methods. Applied Evolutionary Algorithms in JAVA offers a practical, hands-on guide to applying such algorithms to engineering and scientific problems. The concepts are illustrated through clear examples, ranging from simple to more complex problems domains; all based on real-world industrial problems. Examples are taken from image processing, fuzzy-logic control systems, mobile robots, and telecommunication network optimization problems. The JAVA-based toolkit provides an easy-to-use and essential visual interface, with integrated graphing and analysis tools. Topics and features: inclusion of a complete JAVA toolkit for exploring evolutionary algorithms; strong use of visualization techniques, to increase understanding; coverage of all major evolutionary algorithms in common usage; broad range of industrially based example applications; includes examples and an appendix based on fuzzy logic.

Review

From the reviews:

"The book of Ghaenea Hercock gives a good overview of many artificial intelligence approaches, developed in the course of the years. ... Students and computer scientists, who look for an overview of evolutionary algorithms, will receive a well readable composition of the most important procedures." (W. Brecht, Zentralblatt MATH, Vol.1041 (16), 2004)

"This succinct volume from Springer aims to provide an introduction to evolutionary algorithms (both genetic algorithms and genetic programming) from an application point of view, with specific emphasis on the use of the Java programming language. The latter emphasis marks out the book from other more general introductions to the subject ... . In all this is a useful introduction to a fascinating subject. ... it serves as a useful adjunct for those interested in applications and/or Java implementations." (TechBookReport, October, 2003)