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Order statistics in artificial evolution

Abstract : This article deals with the exploitation of statistical information from extremes values of an evolutionary algorithm. One can use the fact that upper order statistics of a sample converge to known distributions for improving efficiency of selection and crossover operators. The work presented in this paper is restricted to criteria defined on real vector spaces. It relies on an underlying canonical model of genetic algorithm, namely tournament selection and uniform crossover. Nevertheless, the results obtained so far encourage further investigations.
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https://hal-enac.archives-ouvertes.fr/hal-01004107
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Stéphane Puechmorel, Daniel Delahaye. Order statistics in artificial evolution. Lecture Notes in Computer Science, Springer, 2003, 2936, pp 51-62. ⟨10.1007/978-3-540-24621-3_5⟩. ⟨hal-01004107⟩

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