  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.
