Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem

By June 10, 2021May 9th, 2023No Comments


“A core challenge of evolutionary search is the need to balance between exploration of the search space and exploitation of highly fit regions. Quality-diversity search has explicitly walked this tightrope between a population’s diversity and its quality. This paper extends a popular quality-diversity search algorithm, MAP-Elites, by treating the selection of parents as a multi-armed bandit problem. Using variations of the upper-confidence bound to select parents from under-explored but  potentially rewarding areas of the search space can accelerate the discovery of new regions as well as improve its archive’s total quality. The paper tests an indirect measure of quality for parent selection: the survival rate of a parent’s offspring. Results show that maintaining a balance between exploration and exploitation leads to the most diverse and high-quality set of solutions in three different testbeds.”


Konstantinos Sfikas, Antonios Liapis and Georgios N. Yannakakis: “Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem” in Proceedings of the Genetic and Evolutionary Computation Conference, 2021


    author={Konstantinos Sfikas and Antonios Liapis},
    title={Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic},
    journal={IEEE Transactions of Games},