Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab

Sai Sumathi S. Sumathi; P. Surekha

ISBN 10: 3540751580 / ISBN 13: 9783540751588

Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2008

About this Item

This is a fine, as new, hardcover first edition, first printing copy, no DJ, maroon spine. 584 pages with index.

Seller Inventory # 105940

Bibliographic Details

• Title: Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab
• Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Publication Date: 2008
• Language: English
• ISBN 10: 3540751580
• ISBN 13: 9783540751588
• Binding: Hardcover
• Edition: 1st Edition
• Condition: Fine
• Dust Jacket Condition: No Jacket

About this title

Synopsis

This book provides a highly accessible introduction to evolutionary computation. It details basic concepts, highlights several applications of evolutionary computation, and includes solved problems using MATLAB software and C/C++. This book also outlines some ideas on when genetic algorithms and genetic programming should be used. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this.

From the Back Cover

This book gives a good introduction to evolutionary computation for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The authors include excellent problem sets, these being divided up into "thought exercises" and "computer exercises" in genetic algorithm. Practical use of genetic algorithms demands an understanding of how to implement them, and the authors do so in the last two chapters of the book by giving the applications in various fields. This book also outlines some ideas on when genetic algorithms and genetic programming should be used, and this is useful since a newcomer to the field may be tempted to view a genetic algorithm as merely a fancy Monte Carlo simulation. The most difficult part of using a genetic algorithm is how to encode the population, and the authors discuss various ways to do this. Various "exotic" approaches to improve the performance of genetic algorithms are also discussed such as the "messy" genetic algorithms, adaptive genetic algorithm and hybrid genetic algorithm.