Abstrakt
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 epsilon-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.