On the impact of a small initial population size in the IPOP active CMA-ES with mirrored mutations on the noiseless BBOB testbed

Abstract

Active Covariance Matrix Adaptation and Mirrored Mutations have been independently proposed as improved variants of the well-known optimization algorithm Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for numerical optimization. This paper investigates the impact of the algorithm's population size when both active covariance matrix adaptation and mirrored mutation are used in the CMA-ES. To this end, we compare the CMA-ES with standard population size λ, i.e., λ = 4 + ⌊ 3 log(D) ⌋ with a version with half this population size where D is the problem dimension.