Krzysztof Krawiec


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Program synthesis tasks usually specify only the desired output of a program and do not state any expectations about its internal behavior. The intermediate execution states reached by a running program can be nonetheless deemed as more or less preferred accordingto their information content with respect to the desired output. In this paper, a consistency measure is proposed that implements this observation. When used as an additional search objective in a typical genetic programming setting, this measure improves the success rate on a suite of 35 benchmarks in a statistically significant way.

@INPROCEEDINGS { LNCS86720434,
    EDITOR = { Thomas Bartz-Beielstein and J\"{u}rgen Branke and Bogdan Filipi\v{c} and Jim Smith },
    BOOKTITLE = { Parallel Problem Solving from Nature -- PPSN XIII },
    PUBLISHER = { Springer },
    LOCATION = { Heidelberg },
    SERIES = { Lecture Notes in Computer Science },
    VOLUME = { 8672 },
    YEAR = { 2014 },
    ISBN = { 978-3-319-10761-5 },
    DOI = { 10.1007/978-3-319-10762-2_43 },
    AUTHOR = { Krzysztof Krawiec and Armando Solar-Lezama },
    TITLE = { Improving Genetic Programming with Behavioral Consistency Measure },
    PAGES = { 434--443 },
    ABSTRACT = { Program synthesis tasks usually specify only the desired output of a program and do not state any expectations about its internal behavior. The intermediate execution states reached by a running program can be nonetheless deemed as more or less preferred accordingto their information content with respect to the desired output. In this paper, a consistency measure is proposed that implements this observation. When used as an additional search objective in a typical genetic programming setting, this measure improves the success rate on a suite of 35 benchmarks in a statistically significant way. },
}


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