论文标题
通过幅度增强多样性
Diversity Enhancement via Magnitude
论文作者
论文摘要
促进和维持候选解决方案的多样性是一般和多目标进化算法的进化算法的关键要求。在本文中，我们使用最近开发的幅度理论来构建梯度流和类似的概念，这些梯度流有系统地操纵欧几里得空间的有限亚集来增强其多样性，并应用思想来服务多目标进化算法。我们使用领先算法在基准问题上展示了多样性，并讨论了框架的扩展。
Promoting and maintaining diversity of candidate solutions is a key requirement of evolutionary algorithms in general and multi-objective evolutionary algorithms in particular. In this paper, we use the recently developed theory of magnitude to construct a gradient flow and similar notions that systematically manipulate finite subsets of Euclidean space to enhance their diversity, and apply the ideas in service of multi-objective evolutionary algorithms. We demonstrate diversity enhancement on benchmark problems using leading algorithms, and discuss extensions of the framework.
