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arXiv · 2201.10037

Diversity Enhancement via Magnitude

Steve Huntsman
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Abstract

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.

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Steve Huntsman. 2022-01-25. Diversity Enhancement via Magnitude. https://doi.org/10.1007/978-3-031-27250-9_27

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