Surrogate-Assisted Partial Order-Based Evolutionary Optimisation

摘要

In this paper, we propose a novel approach (SAPEO) to support the survival selection process in evolutionary multi-objective algorithms with surrogate models. The approach dynamically chooses individuals to evaluate exactly based on the model uncertainty and the distinctness of the population. We introduce multiple SAPEO variants that differ in terms of the uncertainty they allow for survival selection and evaluate their anytime performance on the BBOB bi-objective benchmark. In this paper, we use a Kriging model in conjunction with an SMS-EMOA for SAPEO. We compare the obtained results with the performance of the regular SMS-EMOA, as well as another surrogate-assisted approach. The results open up general questions about the applicability and required conditions for surrogate-assisted evolutionary multi-objective algorithms to be tackled in the future.