Abstract
In this article two aspects of GA are commented from a mathematical point of view. One is concerned with the convergence of GA, and the other is a probabilistic interpretation of the schema theorem. GA produces a stochastic process (that is, Markov chain) of populations. A week sufficient condition which guarantees the convergence to a unique stable distribution will be given which is satisfied by a wide range of current GA. On the other hand, the inequality appeared in the Schema Theorem is not possible to be interpreted from a probabilistic point of view without replacing those random variables by their expectations since the theorem includes random variables. This replacement unfortunately prevents the theorem from being true, and hence it is forced to be revised. Two types of the correct versions of the schema theorem will be given after a rigorous derivation based on a probability theory.
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References
Goldberg. D.E. (1989): Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Publishing Company.
Davis, L. (ed.) (1991): Handbook of genetic algorithms, Van Nostrand Reinhold.
Goldberg, D.E. and Segrest, J.C. (1987): Finite Markov chain analysis of genetic algorithms, Proc. 2nd Int. Conf. Genetic Algorithm, 1–8.
De Jong, K.A. and Spears, W.M. (1990): An analysis of the interacting roles of population size and crossover in genetic algorithms, Proc. 1-st Int. Workshop on Parallel Problem Solving from Nature, Dortmund.
Davis, T.E. and Principe, J.C. (1991): A simulated annealing like convergence theory for the simple genetic algorithm. Proc. 4th Int. Conf. Genetic Algorithm. 174–181.
Eiben, A.E.. Aarts, E.H.L. and Van Hee, K.M. (1991): Global convergence of genetic algorithms: a Markov chain analysis, Lect. Notes Computer Science, Vol 496. 4–12.
Vose, M.D. (1991): Generalizing the notion of schema in genetic algorithms, Artificial Intelligence, 50, 385–396.
Nix, A.E. and Vose, M.D. (1992): Modeling genetic algorithms with Markov chains, Annals of Mathematics and Artificial Intelligence, 5, 79–88.
Yanagiya. H. (1992): Markov analysis of Genetic Algorithms, Technical Report of IEICE, NLP92-26, 19–26 (in Japanese).
Vose, M.D. (1993): Modeling simple genetic algorithms, in Whitley, L.D. (ed.): Foundation of genetic algorithms 2, Morgan Kaufmann.
Suzuki, J. (1995): A Markov chain analysis on simple genetic algorithms, IEEE Trans, on Systems, Man, and Cybernetics, 25, 03.
Kitano, H. (ed.) (1993): Genetic algorithms, Sangyou-Tosho (in Japanese).
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© 1995 Springer-Verlag/Wien
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Uesaka, Y. (1995). Convergence of Algorithm and the Schema Theorem in Genetic Algorithms. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_56
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_56
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
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