Krzysztof Krawiec


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We propose a crossover operator that works with genetic programming trees and is approximately geometric crossover in the semantic space. By defining semantic as program's evaluation profile with respect to a set of fitness cases and constraining to a specific class of metric-based fitness functions, we cause the fitness landscape in the semantic space to have perfect fitness-distance correlation. The proposed approximately geometric semantic crossover exploits this property of the semantic fitness landscape by an appropriate sampling. We demonstrate also how the proposed method may be conveniently combined with hill climbing. We discuss the properties of the methods, and describe an extensive computational experiment concerning logical function synthesis and symbolic regression. #2009GECCOSemanticXoverBib

@INPROCEEDINGS { 2009GECCOSemanticXover,
    AUTHOR = { Krzysztof Krawiec and Pawel Lichocki },
    TITLE = { Approximating geometric crossover in semantic space },
    BOOKTITLE = { GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation },
    YEAR = { 2009 },
    EDITOR = { Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba },
    PAGES = { 987--994 },
    ADDRESS = { Montreal },
    MONTH = { {8-12 } # jul },
    PUBLISHER = { ACM },
    ABSTRACT = { We propose a crossover operator that works with genetic programming trees and is approximately geometric crossover in the semantic space. By defining semantic as program's evaluation profile with respect to a set of fitness cases and constraining to a specific class of metric-based fitness functions, we cause the fitness landscape in the semantic space to have perfect fitness-distance correlation. The proposed approximately geometric semantic crossover exploits this property of the semantic fitness landscape by an appropriate sampling. We demonstrate also how the proposed method may be conveniently combined with hill climbing. We discuss the properties of the methods, and describe an extensive computational experiment concerning logical function synthesis and symbolic regression. },
    1 = { http://doi.acm.org/10.1145/1569901.1570036 },
    BIBSOURCE = { DBLP, http://dblp.uni-trier.de },
    COMMENT = { ProjectELP },
    ISBN13 = { 978-1-60558-325-9 },
    KEYWORDS = { genetic algorithms, genetic programming },
    NOTES = { GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092. },
    ORGANISATION = { SigEvo },
    PUBLISHER_ADDRESS = { New York, NY, USA },
    URL = { http://doi.acm.org/10.1145/1569901.1570036 },
}


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