Proceedings of the First International Conference on Genetic Algorithms and their ApplicationsJohn J. Grefenstette Computer solutions to many difficult problems in science and engineering require the use of automatic search methods that consider a large number of possible solutions to the given problems. This book describes recent advances in the theory and practice of one such search method, called Genetic Algorithms. Genetic algorithms are evolutionary search techniques based on principles derived from natural population genetics, and are currently being applied to a variety of difficult problems in science, engineering, and artificial intelligence. |
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alleles applied approach ARSO average behavior binary bucket brigade classifier system Computer concept consider constraints critical points crossover crossover operator defined described detector diffeomorphism effect environment equivalent evaluation function example expected value experimental function experiments feature space Figure function optimization GA's gene genetic algorithm genetic operators genotype heuristic hillclimbing Holland hyperplanes image registration implementation improve individual KL-ONE knowledge structures layout learning classifier system learning system Machine Learning mapping match score message list method mutation o-schemata object offspring packing parameter parents payoff percent involvement performance PLS1 PLS2 PLSI population position problem production random randomly rapid convergence recombination region set represent representation reward scheme role rule sample search space selection simple simulation solution specific strategy string task taxa taxon techniques Theorem tion tour traveling salesman problem University of Michigan utility variables vector VEGA voter