New PDF release: A genetic algorithm tutorial

By Whitley D.

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Thus CHC uses random selection, except restrictions are imposed on which strings are allowed to mate. Strings with binary encodings must be a certain Hamming distance away from one another before they are allowed to reproduce. This form of \incest prevention" is designed to promote diversity. Eshelman also uses a form of uniform crossover called HUX where exactly half of the di ering bits are swapped during crossover. g. 50) thus using uniform crossover in this context is consistent with DeJong and Spears (1991) conjecture that uniform crossover can provide better sampling coverage in the context of small populations.

Pp 316-337. Muhlenbein, H. (1992) How genetic algorithms really work: I. Mutation and Hillclimbing, Parallel Problem Solving from Nature -2-, R. Manner and B. Manderick, eds. North Holland. Nix, A. and Vose, M. (1992) Modeling Genetic Algorithms with Markov Chains. Annals of Mathematics and Arti cial Intelligence. 5:79-88. 36 Rechenberg, I. (1973) Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog Verland, Stuttgart. D. (1987) Some E ects of Selection Procedures on Hyperplane Sampling by Genetic Algorithms.

1966) Arti cial Intelligence Through Simulated Evolution. John Wiley. , eds. (1992) First Annual Conference on Evolutionary Programming. Goldberg, D. and Bridges, C. (1990) An Analysis of a Reordering Operator on a GA-Hard Problem. Biological Cybernetics, 62:397-405. Goldberg, D. (1987) Simple Genetic Algorithms and the Minimal, Deceptive Problem. In, Genetic Algorithms and Simulated Annealing, L. , Pitman. Goldberg, D. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley.

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A genetic algorithm tutorial by Whitley D.


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