Synopses & Reviews
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 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 them as examples to develop solutions to problems. Volume I offers extensive code lists in a number of languages -- C++, Modl, Quick-Basic, C, LISP, and more. An accompanying diskette contains codes ready to cut and paste, ready-to-run applications, and descriptions of how each code can be implemented. The book does more than just describe GAs. Almost two hundred figures and numerous tables show how they look and work. Volume 11 presents the topic from an applications point of view. The focus is on how to develop genetic algorithm coding schemes and how and when to employ the GA to solve problems.
Table of Contents
v. 1. Applications -- v. 2. New frontiers.