This text introduces the theory, operation, and application of genetic algorithms-search algorithms based on the mechanics of natural selection and genetics. Although genetic algorithms (GAs) are already considered to be an important methodology in the development of search and machine-learning methods, only recently have they received attention in other research and industrial circles. The reliance of GAs on biological metaphor, theory, and terminology, combined with the lack of a basic introduction to the subject, has obscured them from potential users and hidden their value as broadly applicable, powerful techniques that are both easy to understand and to use.

Genetic Algorithms in Search, Optimization, and Machine Learning brings together for the first time, in an informal, tutorial fashion, the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields; programmers, scientists, engineers, mathematicians, statisticians and management scientists will all find interesting possibilities here. The book is suitable both for course work and for self-study, Major concepts are illustrated with running examples, and majo algorithms are illustrated by Pascal computer programs. Chapters conclude with exercises and computer assignments. No prior knowledge of GAs or genetics is assumed, and only a minimu computer programming and mathematics background is required.

Does an exceptional job of making the methods of GAs and classifier systems available to wide audience

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