Finding Influential Variables in Multi-Objective optimization Problems

Updated: Sep 17

Henrik Smedberg & Sunith Bandaru (2020). Finding Influential Variables in Multi-Objective optimization Problems. Conference paper: 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020.


The use of evolutionary algorithms for solving multi-objective optimization problems leaves the decision makers with a set of Pareto-optimal solutions to be considered in their decision making. Multi-objective optimization problems offer two spaces for a decision maker to analyze, the decision space and the objective space. In the literature, most of the focus has been on analyzing the objective space, however, in this paper, two procedures are presented for analyzing the decision space by identifying the variables that predominantly influence the structure of the objective space. Both procedures employ a recently proposed rule mining approach, which is used to find significant rules in terms of the variables. The rules are then combined and an influence score is calculated. The method is demonstrated on four problems, two scalable test problems (DTLZ2 and WFG2) with cases of three, five and seven objectives, one engineering design problem and one simulation-based optimization problem. The experiments show that the proposed approach is able to identify influential variables in most problem cases. Access publication.

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