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Overview
* 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
Synopsis
"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, and<-->rather than divorcing over which computer language codes to provide<-->pseudocodes for customizing GAs. Annotation c. by Book News, Inc., Portland, Or.
Editorials
From the Publisher
"Statisticians and computing scientists will like this book very much and will benefit greatly from it." (Journal of Statistical Computation and Simulation, November 2005)
"…an excellent introduction to the world of optimization with its distinct vocabulary and tools." (Journal of the American Statistical Association, September 2005)
"I recommend it highly to anyone who is interested in trying to explore this powerful tool to optimization problems in his or her area of interest." (International Journal of General Systems, June 2005)
"…a nice step-by-step introduction to genetic algorithms (GA) which is specifically designed for practitioners…" (Journal of Intelligent & Fuzzy Systems, Vol. 16, No. 2, 2005)
"This book is very nice to read. It is ideal for some interesting evening study." (Technometrics, May 2005)
"…this book is a worthwhile addition to any course in optimization and/or Gas. It could also serve as a practical guide and template source for researchers…" (Computing Reviews.com, September 30, 2004)