Measurement of Population Diversity

Abstract

In evolutionary algorithms (EAs), the need to efficiently measure population diversity arises in a variety of contexts, including operator adaptation, algorithm stopping and re-starting criteria, and fitness sharing. In this paper we introduce a unified measure of population diversity and define its relationship to the most common phenotypic and genotypic diversity measures. We further demonstrate that this new measure provides a new and efficient method for computing population diversity, where the cost of computation increases linearly with population size.