MAP-Elites for noisy domains by adaptive sampling

Niels Justesen, Sebastian Risi, Jean-Baptiste Mouret

    Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

    Abstract

    Quality Diversity algorithms (QD) evolve a set of high-performing
    phenotypes that each behaves as differently as possible. However,
    current algorithms are all elitist, which make them unable to cope
    with stochastic fitness functions and behavior evaluations. In fact,
    many of the promising applications of QD algorithms, for instance,
    games and robotics, are stochastic. Here we propose two new extensions to the QD-algorithm MAP-Elites — adaptive sampling and
    drifting-elites — and demonstrate empirically that these extensions
    increase the quality of solutions in a noisy artificial test function
    and the behavioral diversity in a 2D bipedal walker environment.
    Original languageEnglish
    Title of host publicationProceedings of the Genetic and Evolutionary Computation Conference Companion : GECCO '19
    Number of pages2
    PublisherAssociation for Computing Machinery
    Publication date2019
    Pages121-122
    DOIs
    Publication statusPublished - 2019

    Keywords

    • Quality Diversity algorithms
    • Stochastic fitness functions
    • MAP-Elites
    • Adaptive sampling
    • Behavioral diversity

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