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To handle different types of Many-Objective Optimization Problems (MaOPs),Many-Objective Evolutionary Algorithms (MaOEAs) need to simultaneously maintainconvergence and population diversity in the high-dimensional objective space.In order to balance the relationship between diversity and convergence, weintroduce a Kernel Matrix and probability model called Determinantal PointProcesses (DPPs). Our Many-Objective Evolutionary Algorithm with DeterminantalPoint Processes (MaOEADPPs) is presented and compared with severalstate-of-the-art algorithms on various types of MaOPs \textcolor{blue}{withdifferent numbers of objectives}. The experimental results demonstrate thatMaOEADPPs is competitive.

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