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

arXiv: https://arxiv.org/abs/2006.16709 · HTML: https://mlsanity.com/paper/2006.16709/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 4 of 4)

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- [From Performance to Understanding: A Vision for Explainable Automated Algorithm Design](https://mlsanity.com/paper/2511.16201/) (2025)
- [Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution](https://mlsanity.com/paper/2607.29228/) (2026)

## Abstract (arXiv)

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.