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arXiv · 2012.08063

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

Peng Zhang·Jinlong Li·Tengfei Li·Huanhuan Chen
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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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Peng Zhang, Jinlong Li, Tengfei Li, Huanhuan Chen. 2020-12-15. A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes. https://arxiv.org/abs/2012.08063

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