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Genetic fitness optimization using rapidly mixing Markov chains

  • Paul Vitányi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1160)

Abstract

A notion of highly probable fitness optimization through evolutionary computing runs on small size populations in a very general setting is proposed. This has applications to evolutionary learning. Based on rapidly mixing Markov chains, the approach pertains to most types of evolutionary genetic algorithms, genetic programming and the like. For systems having associated rapidly mixing Markov chains and appropriate stationary distributions the new method finds optimal programs (individuals) with probability almost 1. Algorithmically, the novel approach prescribes a strategy of executing many short computation runs, rather than one long computation run. Given an arbitrary evolutionary program it may be infeasible to determine whether its associated matrix is rapidly mixing. In our proposed structured evolutionary program discipline, the development of the program and the guaranty of the rapidly mixing property go hand in hand. We conclude with a tentative toy example.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  1. 1.CWISJ AmsterdamThe Netherlands

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