Krzysztof Krawiec


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We propose an evolutionary framework that uses the set of instructions provided with a genetic programming (GP) problem to automatically build a repertoire of related problems and subsequently uses them to improve the performance of search. The novel idea is to use the synthesised related problems to simultaneously exert multiple selection pressures on the evolving population(s). For that framework, we design two methods. In the first method, individuals optimising for particular problems dwell in separate populations and spawn clones which migrate to other populations, similarly to the island model. The second method operates on a single population and ranks the fitness values that individuals receive from particular problems to make them comparable. When applied to six symbolic regression problems of different difficulty, both methods perform better than the standard GP, though sometimes fail to prove superior to certain control setup. #2010CECKrawiecWielochBib

@INPROCEEDINGS { 2010CECKrawiecWieloch,
    AUTHOR = { Krzysztof Krawiec and Bartosz Wieloch },
    TITLE = { Automatic generation and exploitation of related problems in genetic programming },
    BOOKTITLE = { IEEE Congress on Evolutionary Computation (CEC 2010) },
    YEAR = { 2010 },
    PAGES = { 1248--1255 },
    ADDRESS = { Barcelona, Spain },
    MONTH = { {18-23 } # jul },
    PUBLISHER = { IEEE Press },
    ABSTRACT = { We propose an evolutionary framework that uses the set of instructions provided with a genetic programming (GP) problem to automatically build a repertoire of related problems and subsequently uses them to improve the performance of search. The novel idea is to use the synthesised related problems to simultaneously exert multiple selection pressures on the evolving population(s). For that framework, we design two methods. In the first method, individuals optimising for particular problems dwell in separate populations and spawn clones which migrate to other populations, similarly to the island model. The second method operates on a single population and ranks the fitness values that individuals receive from particular problems to make them comparable. When applied to six symbolic regression problems of different difficulty, both methods perform better than the standard GP, though sometimes fail to prove superior to certain control setup. },
    COMMENT = { ProjectELP },
    DOI = { doi:10.1109/CEC.2010.5586120 },
    ISBN13 = { 978-1-4244-6910-9 },
    KEYWORDS = { genetic algorithms, genetic programming },
    NOTES = { ECJ. WCCI 2010. Also known as \cite{5586120} },
    SIZE = { 8 pages },
}


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