A schema theory analysis of mutation size biases in genetic programming with linear representations

N. F. McPhee, R. Poli, J. E. Rowe

    Research output: Contribution to conferencePaperpeer-review

    11 Scopus citations

    Abstract

    Understanding operator bias in evolutionary computation is important because it is possible for the operator's biases to work against the intended biases induced by the fitness function. In recent work we showed how developments in GP schema theory can be used to better understand the biases induced by the standard subtree crossover when genetic programming is applied to variable length linear structures. In this paper we use the schema theory to better understand the biases induced on linear structures by two common GP subtree mutation operators: FULL and GROW mutation. In both cases we find that the operators do have quite specific biases and typically strongly oversample shorter strings.

    Original languageEnglish (US)
    Pages1078-1085
    Number of pages8
    StatePublished - Jan 1 2001
    EventCongress on Evolutionary Computation 2001 - Seoul, Korea, Republic of
    Duration: May 27 2001May 30 2001

    Conference

    ConferenceCongress on Evolutionary Computation 2001
    CountryKorea, Republic of
    CitySeoul
    Period5/27/015/30/01

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