Aggregate meta-models for evolutionary multiobjective and many-objective optimization

Evolutionary algorithms are among the best multiobjective optimizers. However, they need a large number of function evaluations. In this paper a meta-model based approach to the reduction in the needed number of function evaluations is presented. Local aggregate meta-models are used in a memetic operator. The algorithm is first discussed from a theoretical point of view and then it is shown that the meta-models greatly reduce the number of function evaluations. The approach is compared to a similar one with a single global meta-model as well as to more traditional NSGA-II and ϵ-IBEAϵ-IBEA. Moreover, it is shown that aggregate meta-models work even for a larger number of objectives and therefore should be considered when designing many-objective evolutionary algorithms.