A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization

TL;DR

This survey reviews recent theoretical advances in evolutionary algorithms for discrete optimization, covering runtime analysis, parameter tuning, stochastic and dynamic problems, submodular functions, estimation of distribution algorithms, and drift analysis.

Contribution

It provides a comprehensive overview of recent progress and key techniques in the theoretical understanding of evolutionary algorithms for discrete optimization.

Findings

Advances in fine-grained runtime analysis models

Insights into parameter tuning and control

Progress in analyzing stochastic and dynamic problems

Abstract

The theory of evolutionary computation for discrete search spaces has made significant progress in the last ten years. This survey summarizes some of the most important recent results in this research area. It discusses fine-grained models of runtime analysis of evolutionary algorithms, highlights recent theoretical insights on parameter tuning and parameter control, and summarizes the latest advances for stochastic and dynamic problems. We regard how evolutionary algorithms optimize submodular functions and we give an overview over the large body of recent results on estimation of distribution algorithms. Finally, we present the state of the art of drift analysis, one of the most powerful analysis technique developed in this field.