Solution Subset Selection for Final Decision Making in Evolutionary
Multi-Objective Optimization
- URL: http://arxiv.org/abs/2006.08156v1
- Date: Mon, 15 Jun 2020 06:26:58 GMT
- Title: Solution Subset Selection for Final Decision Making in Evolutionary
Multi-Objective Optimization
- Authors: Hisao Ishibuchi and Lie Meng Pang and Ke Shang
- Abstract summary: We discuss subset selection from a viewpoint of the final decision making.
We show that the formulated function is the same as the IGD plus indicator.
- Score: 7.745468825770201
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In general, a multi-objective optimization problem does not have a single
optimal solution but a set of Pareto optimal solutions, which forms the Pareto
front in the objective space. Various evolutionary algorithms have been
proposed to approximate the Pareto front using a pre-specified number of
solutions. Hundreds of solutions are obtained by their single run. The
selection of a single final solution from the obtained solutions is assumed to
be done by a human decision maker. However, in many cases, the decision maker
does not want to examine hundreds of solutions. Thus, it is needed to select a
small subset of the obtained solutions. In this paper, we discuss subset
selection from a viewpoint of the final decision making. First we briefly
explain existing subset selection studies. Next we formulate an expected loss
function for subset selection. We also show that the formulated function is the
same as the IGD plus indicator. Then we report experimental results where the
proposed approach is compared with other indicator-based subset selection
methods.
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