Krzysztof Krawiec


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Online progress in search and optimization is often hindered by neutrality in the fitness landscape, when many genotypes map to the same fitness value. We propose a method for imposing a gradient on the fitness function of a metaheuristic (in this case, Genetic Programming) via a metric (Minimum Description Length) induced from patterns detected in the trajectory of program execution. These patterns are induced via a decision tree classifier. We apply this method to a range of integer and boolean-valued problems, significantly outperforming the standard approach. The method is conceptually straightforward and applicable to virtually any metaheuristic that can be appropriately instrumented.

@INPROCEEDINGS { Krawiec:2013:PGP:2463372.2463496,
    AUTHOR = { Krawiec, Krzysztof and Swan, Jerry },
    TITLE = { Pattern-guided genetic programming },
    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 = { 949--956 },
    NUMPAGES = { 8 },
    URL = { http://doi.acm.org/10.1145/2463372.2463496 },
    DOI = { 10.1145/2463372.2463496 },
    ACMID = { 2463496 },
    PUBLISHER = { ACM },
    ADDRESS = { New York, NY, USA },
    KEYWORDS = { genetic programming, mdl, neutrality, program trace, push },
    ABSTRACT = { Online progress in search and optimization is often hindered by neutrality in the fitness landscape, when many genotypes map to the same fitness value. We propose a method for imposing a gradient on the fitness function of a metaheuristic (in this case, Genetic Programming) via a metric (Minimum Description Length) induced from patterns detected in the trajectory of program execution. These patterns are induced via a decision tree classifier. We apply this method to a range of integer and boolean-valued problems, significantly outperforming the standard approach. The method is conceptually straightforward and applicable to virtually any metaheuristic that can be appropriately instrumented. },
}


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