论文标题
一种基于确定点过程的新的多目标进化算法
A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes
论文作者
论文摘要
为了处理不同类型的多个目标优化问题（MAOPS），多目标进化算法（MAOEAS）需要同时维持高维度空间中的收敛性和种群多样性。为了平衡多样性与收敛之间的关系，我们引入了一个称为确定点过程（DPPS）的内核矩阵和概率模型。提出了我们带有确定点过程（MaoEadpps）的多个目标进化算法，并与各种Maops \ TextColor {blue} {具有不同数量的目标}上的几种最先进的算法进行了比较。实验结果表明，毛主义是有竞争力的。
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.
