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.
@article{arxiv.2012.08063,
title = {A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes},
author = {Peng Zhang and Jinlong Li and Tengfei Li and Huanhuan Chen},
journal= {arXiv preprint arXiv:2012.08063},
year = {2020}
}