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 1 TO GENETIC ALGORITHMS | 1 |
MATHEMATICAL FOUNDATIONS | 27 |
COMPUTER IMPLEMENTATION | 59 |
Copyright | |
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Common terms and phrases
allele applications apportionment of credit average fitness begin binary bucket brigade building blocks calculate chapter chrom chromosome clearingrec coding coefficients Conference on Genetic cross crossover decode default hierarchy defining length detector diploid dominance ebid end end environment environrec epistasis example FIGURE fitness function fitness values function evaluation GBML gene genetic operators Goldberg Grefenstette haploid Holland implementation individual initial integer inversion iteration Jong Jong's KL-ONE knapsack problem machine learning match mating methods multiplexer mutation number of schemata objective function offspring optimization parameter partition coefficients Pascal payoff performance permits popsize population position probability problem procedure proportion pseudorandom random number Reprinted by permission reproduction roulette wheel roulette wheel selection routines sampling schema schema H schemata selection shown in Fig simple classifier system simple genetic algorithm simulation single strength string structure sumfitness techniques tion traveling salesman problem variable writeln(1st writeln(rep