Genetic Algorithms in Search, Optimization, and Machine Learning

This book brings together - in an informal and 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. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required. 0201157675B07092001 This book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required Funded By Dsu Title Iii 2007-2012. 1. A Gentle Introduction To Genetic Algorithms --- 2. Genetic Algorithms Revisited: Mathematical Foundations --- 3. Computer Implementation Of A Genetic Algorithm --- 4. Some Applications Of Genetic Algorithms --- 5. Advanced Operators And Techniques In Genetic Search --- 6. Introduction To Genetics-based Machine Learning --- 7. Applications Of Genetics-based Machine Learning --- 8. A Look Back, A Glance Ahead. By David E. Goldberg. Includes Index. Bibliography: P. [381]-401.