Abstrakt
Evolutionary algorithms are among the best multi-objective optimizers, but the large number of objective function evaluations they require makes it hard to use them to solve certain real-life tasks. In this work we present a surrogate-based local search for the multi-objective covariance matrix adaption evolution strategy (MO-CMA-ES).
The local search is based on the estimation of hypervolume contribution of each individual and maximization of this contribution. This work extends our previous work and makes such surrogate models applicable to problems with more than two objectives.
Moreover, it uses a unique feature of MO-CMA-ES to make the local search more effective. The results indicate that the algorithm can find solutions of the same quality as MO-CMA-ES while using 30-50 percent less objective function evaluations.