Capturing Relationships in Multi-objective Optimization


When applied to multi-objective problems (MOPs), evolutionary algorithms (EAs) can be noticeably improved by representing and exploiting information about the interactions between the components of the problem (variables and objectives). However, accurate detection of such relationships is a challenging question that involves other related issues such as finding the right metric for measuring the interaction, deciding about the timing for testing the interactions, and deciding on appropriate ways to represent the relationships found. In this paper we investigate the performance of three correlation measures (Kendall, Spearman and Pearson) in the context of multi-objective optimization using the MOEA/D-DRA algorithm. We analyze the accuracy of the measures at different stages of the evolution and for different types of relationships. Moreover, the paper proposes a meaningful way for visualizing and interpreting the captured interactions.