Krzysztof Krawiec


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A common approach in Geometric Semantic Genetic Programming (GSGP) is to seed initial populations using conventional, semantic-unaware methods like Ramped Half-and-Half. We formally demonstrate that this may limit GSGP's ability to find a program with the sought semantics. To overcome this issue, we determine the desired properties of geometric-aware semantic initialization and implement them in Semantic Geometric Initialization (\textsc{Sgi}) algorithm, which we instantiate for symbolic regression and Boolean function synthesis problems. Properties of \textsc{Sgi} and its impact on GSGP search are verified experimentally on nine symbolic regression and nine Boolean function synthesis benchmarks. When assessed experimentally, \textsc{Sgi} leads to superior performance of GSGP search: better best-of-run fitness and higher probability of finding the optimal program.

@INPROCEEDINGS { Pawlak2:2016:EuroGP,
    ABSTRACT = { A common approach in Geometric Semantic Genetic Programming (GSGP) is to seed initial populations using conventional, semantic-unaware methods like Ramped Half-and-Half. We formally demonstrate that this may limit GSGP's ability to find a program with the sought semantics. To overcome this issue, we determine the desired properties of geometric-aware semantic initialization and implement them in Semantic Geometric Initialization (\textsc{Sgi}) algorithm, which we instantiate for symbolic regression and Boolean function synthesis problems. Properties of \textsc{Sgi} and its impact on GSGP search are verified experimentally on nine symbolic regression and nine Boolean function synthesis benchmarks. When assessed experimentally, \textsc{Sgi} leads to superior performance of GSGP search: better best-of-run fitness and higher probability of finding the optimal program. },
    ADDRESS = { Porto, Portugal },
    AUTHOR = { Tomasz P. Pawlak and Krzysztof Krawiec },
    BOOKTITLE = { EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming },
    EDITOR = { Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa },
    KEYWORDS = { genetic algorithms, genetic programming },
    MONTH = { {30 } # mar # {--1 } # apr },
    NOTES = { Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016 },
    ORGANISATION = { EvoStar },
    PAGES = { 254--269 },
    PUBLISHER = { Springer Verlag },
    SERIES = { LNCS },
    TITLE = { Semantic Geometric Initialization },
    VOLUME = { 9594 },
    YEAR = { 2016 },
}


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