Surrogate-Assisted Partial Order-based Evolutionary Optimisation
This work addresses efficiency challenges in multi-objective optimization for researchers and practitioners, but it is incremental as it builds on existing surrogate-assisted methods.
The paper tackles the problem of improving survival selection in multi-objective evolutionary algorithms by proposing SAPEO, a surrogate-assisted approach that dynamically selects individuals for exact evaluation based on model uncertainty and population distinctness, with results evaluated on the BBOB bi-objective benchmark.
In this paper, we propose a novel approach (SAPEO) to support the survival selection process in multi-objective evolutionary algorithms with surrogate models - it dynamically chooses individuals to evaluate exactly based on the model uncertainty and the distinctness of the population. We introduce variants that differ in terms of the risk they allow when doing survival selection. Here, the anytime performance of different SAPEO variants is evaluated in conjunction with an SMS-EMOA using the BBOB bi-objective benchmark. 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 multi-objective evolutionary algorithms to be tackled in the future.