Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems
Recently, it has been demonstrated that a solution set that is better than the final population can be obtained by subset selection in some studies on evolutionary multi-objective optimization. The main challenge in this type of subset selection is how to efficiently handle a huge candidate solution set, especially when the hypervolume-based subset selection is used for many-objective optimization. In this paper, we propose an efficient two-stage greedy algorithm for hypervolume-based subset selection. In each iteration of the proposed greedy algorithm, a small number of promising candidate solutions are selected in the first stage using the rough hypervolume contribution approximation. In the second stage, a single solution among them is selected using the more precise approximation. Experimental results show that the proposed algorithm is much faster than state-of-the-art hypervolume-based greedy subset selection algorithms at the cost of a slight deterioration of the selected subset quality.
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|Document Type||Conference paper|
|Department||Department of Computer Science and Engineering|
Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
|First Author Affilication||Department of Computer Science and Engineering|
|Corresponding Author Affilication||Department of Computer Science and Engineering|
|First Author's First Affilication||Department of Computer Science and Engineering|
Nan，Yang,Ishibuchi，Hisao,Shu，Tianye,et al. Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems[C],2023:391-404.
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