A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes

• URL: http://arxiv.org/abs/2012.08063v1
• Date: Tue, 15 Dec 2020 03:22:06 GMT
• Title: A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes
• Authors: Peng Zhang, Jinlong Li, Tengfei Li, Huanhuan Chen
• Abstract summary: We introduce a Kernel Matrix and probability model called Determinantal Point Processes (DPPs) Our Many-Objective Evolutionary Algorithm with Determinantal Point Processes (MaOEADPPs) is presented and compared with several state-of-the-art algorithms.
• Score: 31.00549172139366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To handle different types of Many-Objective Optimization Problems (MaOPs), Many-Objective Evolutionary Algorithms (MaOEAs) need to simultaneously maintain convergence and population diversity in the high-dimensional objective space. In order to balance the relationship between diversity and convergence, we introduce a Kernel Matrix and probability model called Determinantal Point Processes (DPPs). Our Many-Objective Evolutionary Algorithm with Determinantal Point Processes (MaOEADPPs) is presented and compared with several state-of-the-art algorithms on various types of MaOPs \textcolor{blue}{with different numbers of objectives}. The experimental results demonstrate that MaOEADPPs is competitive.

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