Investigation of strategies for an increasing population size in multi-objective CMA-ES
Steffen Limmer, Dietmar Fey · 2016
The Multi-objective Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES) is an evolutionary algorithm for continuous vector optimization. It is invariant against rotations and translations of the search space and empirical evaluations have shown that it is very competitive with other popular multi-objective evolutionary algorithms, like NSGA-II. However, MO-CMA-ES requires a certain “warm-up phase” to adapt internal strategy parameters. A promising approach to speed up this “warm-up phase” is to keep the start population small and to gradually increase its size during the optimization. We experimentally investigate two static and three dynamic strategies for increasing the population size. The results show that the employment of a dynamic population size increasing strategy can significantly improve the performance of MO-CMA-ES, especially when the budget of objective function evaluations is small.