  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.
