Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization

• URL: http://arxiv.org/abs/2201.06700v1
• Date: Tue, 18 Jan 2022 02:09:08 GMT
• Title: Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
• Authors: Ke Shang and Tianye Shu and Hisao Ishibuchi and Yang Nan and Lie Meng Pang
• Abstract summary: In the evolutionary multi-objective optimization (EMO) field, the standard practice is to present the final population of an EMO algorithm as the output. Recently, a new EMO framework has been proposed to solve this issue by storing all the non-dominated solutions generated during the evolution in an archive. This paper proposes a benchmark test suite for subset selection from large candidate solution sets.
• Score: 6.544757635738911
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the evolutionary multi-objective optimization (EMO) field, the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown that the final population often includes solutions which are dominated by other solutions generated and discarded in previous generations. Recently, a new EMO framework has been proposed to solve this issue by storing all the non-dominated solutions generated during the evolution in an archive and selecting a subset of solutions from the archive as the output. The key component in this framework is the subset selection from the archive which usually stores a large number of candidate solutions. However, most studies on subset selection focus on small candidate solution sets for environmental selection. There is no benchmark test suite for large-scale subset selection. This paper aims to fill this research gap by proposing a benchmark test suite for subset selection from large candidate solution sets, and comparing some representative methods using the proposed test suite. The proposed test suite together with the benchmarking studies provides a baseline for researchers to understand, use, compare, and develop subset selection methods in the EMO field.

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