Addressing Limitations and Inaccuracy of Diversity Metrics in Evolutionary Algorithms with an Accurate Dimension-Wise Diversity

Maintaining population diversity in evolutionary computation poses a significant challenge throughout the optimization process. A balanced exploration-exploitation dynamic is crucial for achieving optimal results, and a diverse population proportion plays a key role in achieving this balance. Over the years, various methods have been proposed to evaluate diversity. However, relying on a single value to gauge population diversity has its limitations. This paper explores the shortcomings of existing population diversity metrics through extensive experimentation with different population configurations. The findings reveal that some metrics may be misleading, as a lower diversity value does not necessarily imply poor performance in solving optimization problems. In response to these challenges, this paper introduces a new and improved diversity metric, building upon the Dimension-Wise Diversity notion. The proposed metric corrects the shortcomings associated with its predecessors, leading to a more accurate interpretation of diversity values.