# Theory of Estimation-of-Distribution Algorithms

arXiv: https://arxiv.org/abs/1806.05392 · HTML: https://mlsanity.com/paper/1806.05392/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [When Non-Elitism Meets Time-Linkage Problems](https://mlsanity.com/paper/2104.06831/) (2021)
- [From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms](https://mlsanity.com/paper/2206.09090/) (2022)
- [General Univariate Estimation-of-Distribution Algorithms](https://mlsanity.com/paper/2206.11198/) (2022)
- [DIAS: A Domain-Independent Alife-Based Problem-Solving System](https://mlsanity.com/paper/2203.06855/) (2022)
- [Zero-Inflated Gaussian Distributions Enable Parameter-Space Sparsity in Estimation-of-Distribution Algorithms](https://mlsanity.com/paper/2606.19369/) (2026)

## Abstract (arXiv)

Estimation-of-distribution algorithms (EDAs) are general metaheuristics used in optimization that represent a more recent alternative to classical approaches like evolutionary algorithms. In a nutshell, EDAs typically do not directly evolve populations of search points but build probabilistic models of promising solutions by repeatedly sampling and selecting points from the underlying search space. Recently, there has been made significant progress in the theoretical understanding of EDAs. This article provides an up-to-date overview of the most commonly analyzed EDAs and the most recent theoretical results in this area. In particular, emphasis is put on the runtime analysis of simple univariate EDAs, including a description of typical benchmark functions and tools for the analysis. Along the way, open problems and directions for future research are described.