The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses

TL;DR

This survey reviews rigorous runtime analyses demonstrating that population diversity in evolutionary algorithms enhances global exploration, prevents premature convergence, and improves performance across various optimization scenarios.

Contribution

It compiles and discusses existing rigorous analyses showing the benefits of population diversity, providing a foundational understanding of its role in evolutionary algorithms.

Findings

Diversity aids in finding multiple global optima.

Diversity improves crossover effectiveness.

Diversity is crucial in dynamic and multiobjective optimization.

Abstract

Population diversity is crucial in evolutionary algorithms to enable global exploration and to avoid poor performance due to premature convergence. This book chapter reviews runtime analyses that have shown benefits of population diversity, either through explicit diversity mechanisms or through naturally emerging diversity. These works show that the benefits of diversity are manifold: diversity is important for global exploration and the ability to find several global optima. Diversity enhances crossover and enables crossover to be more effective than mutation. Diversity can be crucial in dynamic optimization, when the problem landscape changes over time. And, finally, it facilitates search for the whole Pareto front in evolutionary multiobjective optimization. The presented analyses rigorously quantify the performance of evolutionary algorithms in the light of population diversity, laying the foundation for a rigorous understanding of how search dynamics are affected by the presence or absence of population diversity and the introduction of diversity mechanisms.