Improvement of Rough Set-based Search Space Reduction for Surrogate-assisted Multi-objective Evolutionary Algorithms

Abstract

Surrogate-assisted evolutionary algorithms (SAEAs) are effective approaches for solving expen-(breakpoint)sive multi-objective optimization problems (EMOPs). Many existing SAEAs have focused on improving the quality of surrogate models, but less attention has been dedicated to effectively reducing search spaces. In this paper, we propose an extension of our space reduction technique and incorporate it into a typical SAEA, K-RVEA. Specifically, the proposed algorithm uses both good and bad rules derived from rough set theory to reduce the search area and facilitate more efficient search. Experimental results show that the performance of K-RVEA is improved by incorporating search area reduction by rough sets.