Krzysztof Krawiec


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In evolutionary computation, the fitness of a candidate solutionfunction conveys sparse feedback. Yet in many cases, candidate solutions can potentially yield more information. In genetic programming (GP), one can easily examine program behavior on particular fitness cases or at intermediate execution states. However, how to exploit it to effectively guide the search remains unclear. In this study we apply machine learning algorithms to features describing the intermediate behavior of the executed program. We then drive the standard evolutionary search with additional objectives reflecting this intermediate behavior. The machine learning functions independent of task-specific knowledge and discovers potentially useful components of solutions (subprograms), which we preserve in an archive and use as building blocks when composing new candidate solutions. In an experimental assessment on a suite of benchmarks, the proposed approach proves more capable of finding optimal and/or well-performing solutions than control methods.

@INPROCEEDINGS { KrawiecOReily:2014:GECCO,
    AUTHOR = { Krzysztof Krawiec and Una-May O'Reilly },
    TITLE = { Behavioral Programming: A Broader and More Detailed Take on Semantic {GP} },
    BOOKTITLE = { Proceeding of the sixteenth annual conference on Genetic and evolutionary computation conference },
    SERIES = { GECCO '14 },
    YEAR = { 2014 },
    LOCATION = { Vancouver, Canada },
    NUMPAGES = { 8 },
    URL = { http://dx.doi.org/10.1145/2576768.2598288 },
    DOI = { dx.doi.org/10.1145/2576768.2598288 },
    PUBLISHER = { ACM },
    ADDRESS = { New York, NY, USA },
    KEYWORDS = { program synthesis; genetic programming; program semantics; behavioral evaluation; search operators; archive; multi-objective evolutionary computation },
    ABSTRACT = { In evolutionary computation, the fitness of a candidate solutionfunction conveys sparse feedback. Yet in many cases, candidate solutions can potentially yield more information. In genetic programming (GP), one can easily examine program behavior on particular fitness cases or at intermediate execution states. However, how to exploit it to effectively guide the search remains unclear. In this study we apply machine learning algorithms to features describing the intermediate behavior of the executed program. We then drive the standard evolutionary search with additional objectives reflecting this intermediate behavior. The machine learning functions independent of task-specific knowledge and discovers potentially useful components of solutions (subprograms), which we preserve in an archive and use as building blocks when composing new candidate solutions. In an experimental assessment on a suite of benchmarks, the proposed approach proves more capable of finding optimal and/or well-performing solutions than control methods. },
}


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