Oversampling-Guided Search for Evolutionary Multiobjective Optimization

This paper proposes an oversampling-guided search framework to improve the search efficiency of multiobjective evolutionary algorithms (MOEAs) for high-dimensional prob-lems. The proposed algorithm uses an oversampling algorithm to generate artificial samples of obtained Pareto solutions, and then it uses those as parent solutions to guide the evolutionary search to good subspaces that have not been sufficiently explored. We in-corporate the proposed algorithm into two popular MOEAs, i.e., NSGA-II and IBEA. Experimental results show that the proposed algorithm significantly improves the performance of NSGA-II and IBEA on problems with up to 200 decision variables. We also discuss a possible direction for further improvement while demonstrating the drawbacks of using oversampling techniques.