Practical Genetic Algorithms

* This book deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science * Most significant update to the second edition is the MATLAB codes that accompany the text * Provides a thorough discussion of hybrid genetic algorithms * Features more examples than first edition

"The first introductory-level book to emphasize practical applications through the use of example problems."

–– International Journal of General Systems, Vol. 31, No. 1, 2002, on the first edition

The use of genetic algorithms (GAs) to solve large and often complex computational problems has given rise to many new applications in a variety of disciplines. Practical Genetic Algorithms was the first introductory-level book on genetic algorithms to emphasize practical applications rather than theory. Practical Genetic Algorithms, Second Edition reflects the significant evolution of the field since the book’s first edition.

In an accessible style, the authors explain why the genetic algorithm is superior in many real-world applications, cover continuous parameter genetic algorithms, and provide in-depth trade-off analysis of genetic algorithm parameter selection. This Second Edition features:

• Numerous practical example problems
• A CD-ROM with MATLAB and High Performance Fortran codes
• A new, more complete picture of traditional optimization
• Revised examples reflecting recent research
• Coverage of pareto-genetic and hybrid genetic algorithms (GAs)
• New sections on hybrid GAs, parallel GAs, and messy GAs, with recommendations on improving their performance
• An all new chapter on simulated annealing, ant-colony optimization, evolutionary strategies, and other cutting-edge artificial intelligence methods of optimization

Written for the practicing scientist, engineer, economist, artist, or anyone with an interest in the basics of GAs, the second edition continues to offer readers an up-to-date look at the evolving practical applications of GAs and how to manipulate them in order to get the best performance.

Booknews

Per the glossary of this introductory text, a genetic algorithm (GA) is a type of computation that models the biological genetic process by including crossover and mutation operators. R. Haupt (electrical engineering, U. of Nevada, Reno) and S. Haupt (atmospheric and oceanic science, U. of Colorado, Boulder) explain GA parameters, applications, and trends in computer modeling of natural processes. Includes a list of symbols, andrather than divorcing over which computer language codes to providepseudocodes for customizing GAs. Annotation c. by Book News, Inc., Portland, Or.

Practical Genetic Algorithms is the first introductory-level book to emphasize practical applications through the use of example problems. In an accessible style, the authors explain why the genetic algorithm is superior in many real-world applications, cover continuous parameter genetic algorithms, and provide in-depth trade-off analysis of genetic algorithm parameter selection. Written for the end user in engineering, science, and computer programming, as well as upper-level undergraduate and graduate students, Practical Genetic Algorithms provides numerous practical example problems; contains over 80 illustrations; features many figures and tables; and includes three appendices: a glossary of terms, a list of genetic algorithm routines in pseudocode, and a list of symbols used in the book. Randy Haupt and Sue Ellen Haupt, both affiliated with The Pennsylvania State University Applied Research Laboratory, emphasize practical applications rather than theory in this second edition of a book/CD- ROM guide for practicing scientists, engineers, economists, artists, and others interested in the basics of genetic algorithms (GAs). This edition contains code, in both MATLAB and High-Performance Fortran, on the CD-ROM, plus expanded information on methods for improving GA performance, and a new chapter on other artificial intelligence methods of optimization such as simulated annealing and ant colony optimization. Chapter exercises allow the book to be used as a text. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com) Deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science. This book includes the MATLAB codes and provides a discussion of hybrid genetic algorithms, as well as more examples. It also gives an introduction to the subject of genetic algorithms.