Genetic Algorithms in Search, Optimization, and Machine LearningA gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis. |
Contents
A GENTLE INTRODUCTION TO GENETIC ALGORITHMS | 1 |
MATHEMATICAL FOUNDATIONS | 27 |
A GENETIC ALGORITHM | 59 |
Copyright | |
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Common terms and phrases
action active adaptive allele applications artificial assume average begin better binary blocks calculate called chapter chromosome classifier system coding coefficients Compare complex condition consider contains Continued count cross crossover decode defined detector dominance effect environment evaluation event examine example expected experiments FIGURE fitness function gene genetic algorithm Goldberg Holland implementation important improvement individual initial integer inversion learning length machine match mating mechanism methods Michigan mutation natural objective operators optimization parallel parameter particular performance permits population position possible presented probability problem procedure proportion random reproduction roulette wheel selection routines rules sampling scaling schema schemata selection sharing shown in Fig similar simple simulation single space specificity strength string structure techniques theory tion trials University values variable writeln(rep