Geometric particle swarm optimization for the sudoku puzzle

Alberto Moraglio, Julian Togelius

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Geometric particle swarm optimization (GPSO) is a recentlyintroduced generalization of traditional particle swarm optimization(PSO) that applies to all combinatorial spaces. The aim of thispaper is to demonstrate the applicability of GPSO to non-trivialcombinatorial spaces. The Sudoku puzzle is a perfect candidate totest new algorithmic ideas because it is entertaining andinstructive as well as a non-trivial constrained combinatorialproblem. We apply GPSO to solve the sudoku puzzle.

    Original languageEnglish (US)
    Title of host publicationProceedings of GECCO 2007
    Subtitle of host publicationGenetic and Evolutionary Computation Conference
    Pages118-125
    Number of pages8
    DOIs
    StatePublished - 2007
    Event9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 - London, United Kingdom
    Duration: Jul 7 2007Jul 11 2007

    Publication series

    NameProceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

    Other

    Other9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
    CountryUnited Kingdom
    CityLondon
    Period7/7/077/11/07

    Keywords

    • Geometric crossover
    • Metric space
    • Particle swarm
    • Sudoku

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software
    • Theoretical Computer Science

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  • Cite this

    Moraglio, A., & Togelius, J. (2007). Geometric particle swarm optimization for the sudoku puzzle. In Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference (pp. 118-125). (Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/1276958.1276975