Krzysztof Krawiec


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We propose a new means of executing a genetic program which improves its output quality. Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and linearly combines a program's subexpressions via multiple regression on the target variable. The regression yields an alternate output: the prediction of the resulting multiple regression model. It is this output, over many fitness cases, that we assess for fitness, rather than the program's execution output. MRGP can be used to improve the fitness of a final evolved solution. On our experimental suite, MRGP consistently generated solutions fitter than the result of competent GP or multiple regression. When integrated into GP, \inline MRGP, on the basis of equivalent computational budget, outperforms competent GP while also besting \posthoc MRGP. Thus MRGP's output method is shown to be superior to the output of program execution and it represents a practical, cost neutral, improvement to GP.

@INPROCEEDINGS { ArnaldoKrawiecOReily:2014:GECCO,
    AUTHOR = { Ignacio Arnaldo and Krzysztof Krawiec and Una-May O'Reilly },
    TITLE = { Multiple Regression Genetic Programming },
    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/10.1145/2576768.2598291 },
    DOI = { dx.doi.org/10.1145/2576768.2598291 },
    PUBLISHER = { ACM },
    ADDRESS = { New York, NY, USA },
    KEYWORDS = { Genetic Programming, Multiple Regression },
    ABSTRACT = { We propose a new means of executing a genetic program which improves its output quality. Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and linearly combines a program's subexpressions via multiple regression on the target variable. The regression yields an alternate output: the prediction of the resulting multiple regression model. It is this output, over many fitness cases, that we assess for fitness, rather than the program's execution output. MRGP can be used to improve the fitness of a final evolved solution. On our experimental suite, MRGP consistently generated solutions fitter than the result of competent GP or multiple regression. When integrated into GP, \inline MRGP, on the basis of equivalent computational budget, outperforms competent GP while also besting \posthoc MRGP. Thus MRGP's output method is shown to be superior to the output of program execution and it represents a practical, cost neutral, improvement to GP. },
}


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