Practical Handbook of Genetic Algorithms: New Frontiers Vol 2 (Practical Handbook of Genetic Algorithms Vol. 2) (Hardcover)

Product Description
Presenting the topic from a an applications point of view, this text takes readers from the construction of a simple GA to advanced implementations. As readers come to understand GAs and their processes, they will begin to understand the power of the genetic-based problem-solving paradigms that lie behind them. Problems that for many have been considered intractable are shown to be solvable using the techniques described in this work. Specific solution descriptions to real-world problems are provided, or use these as examples to develop solutions to unique problems. The book demonstrates how to develop genetic algorithm coding schemes and covers how and when to employ the Genetic Algorithm to solve problems.

From the Back Cover
The mathematics employed by genetic algorithms (GAs)are among the most exciting discoveries of the last few decades. But what exactly is a genetic algorithm? A genetic algorithm is a problem-solving method that uses genetics as its model of problem solving. It applies the rules of reproduction, gene crossover, and mutation to pseudo-organisms so those "organisms" can pass beneficial and survival-enhancing traits to new generations. GAs are useful in the selection of parameters to optimize a system's performance. A second potential use lies in testing and fitting quantitative models. Unlike any other book available, this interesting new text/reference takes you from the construction of a simple GA to advanced implementations. As you come to understand GAs and their processes, you will begin to understand the power of the genetic-based problem-solving paradigms that lie behind them.
The Practical Handbook of Genetic Algorithms presents for the first time new areas of research and implementation. Problems that for many have been considered intractable are shown to be solvable using the techniques described in this work. Specific solution descriptions to real-world problems are provided, or use these as examples to develop solutions to unique problems.
Volume II picks up where the first book leaves off and presents the topic from more of an applications point of view. The focus of the book is to show the reader how to develop their own genetic algorithm coding schemes and how and when to employ the GA to solve problems.

First Sentence
Multi-Niche Crowding (MNC) is a new genetic algorithm designed for locating multiple peaks in a multimodal function. Read the first page Browse Sample Pages
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover

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