Krzysztof Krawiec


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We propose a novel crossover operator for tree-based genetic programming, that produces approximately geometric offspring. We empirically analyze certain aspects of geometry of crossover operators and verify performance of the new operator on both, training and test fitness cases coming from set of symbolic regression benchmarks. The operator shows superior performance and higher probability of producing geometric offspring than tree-swapping crossover and other semantic-aware control methods.

@INPROCEEDINGS { Krawiec:2013:AGC:2463372.2463483,
    AUTHOR = { Krawiec, Krzysztof and Pawlak, Tomasz },
    TITLE = { Approximating geometric crossover by semantic backpropagation },
    BOOKTITLE = { Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference },
    SERIES = { GECCO '13 },
    YEAR = { 2013 },
    ISBN = { 978-1-4503-1963-8 },
    LOCATION = { Amsterdam, The Netherlands },
    PAGES = { 941--948 },
    NUMPAGES = { 8 },
    URL = { http://doi.acm.org/10.1145/2463372.2463483 },
    DOI = { 10.1145/2463372.2463483 },
    ACMID = { 2463483 },
    PUBLISHER = { ACM },
    ADDRESS = { New York, NY, USA },
    KEYWORDS = { genetic programming, geometric crossover, program semantics },
    ABSTRACT = { We propose a novel crossover operator for tree-based genetic programming, that produces approximately geometric offspring. We empirically analyze certain aspects of geometry of crossover operators and verify performance of the new operator on both, training and test fitness cases coming from set of symbolic regression benchmarks. The operator shows superior performance and higher probability of producing geometric offspring than tree-swapping crossover and other semantic-aware control methods. },
}


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