Covariance Matrix Adaptation MAP-Annealing
Matthew C. Fontaine, Stefanos Nikolaidis · Proceedings of the Genetic and Evolutionary Computation Conference · 2023
Single-objective optimization algorithms search for the single highestquality solution with respect to an objective.Quality diversity (QD) optimization algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME), search for a collection of solutions that are both high-quality with respect to an objective and diverse with respect to specified measure functions.However, CMA-ME suffers from three major limitations highlighted by the QD community: prematurely abandoning the objective in favor of exploration, struggling to explore flat objectives, and having poor performance for lowresolution archives.We propose a new quality diversity algorithm, Covariance Matrix Adaptation MAP-Annealing (CMA-MAE), that addresses all three limitations.We provide theoretical justifications for the new algorithm with respect to each limitation.Our theory informs our experiments, which support the theory and show that CMA-MAE achieves state-of-the-art performance and robustness.