Diversity Enhancement via Magnitude
This addresses diversity maintenance in evolutionary algorithms, which is crucial for optimization performance, though it appears incremental as it builds on existing magnitude theory and applies it to known algorithms.
The paper tackled the problem of maintaining diversity in evolutionary algorithms by applying magnitude theory to construct gradient flows that manipulate solution sets, demonstrating diversity enhancement on benchmark problems with leading multi-objective evolutionary algorithms.
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.