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


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
Publication statusPublished - 2019


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