# General Univariate Estimation-of-Distribution Algorithms

arXiv: https://arxiv.org/abs/2206.11198 · HTML: https://mlsanity.com/paper/2206.11198/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [The Runtime of the Compact Genetic Algorithm on Jump Functions](https://mlsanity.com/paper/1908.06527/) (2019)
- [On the Limitations of the Univariate Marginal Distribution Algorithm to Deception and Where Bivariate EDAs might help](https://mlsanity.com/paper/1907.12438/) (2019)
- [Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms](https://mlsanity.com/paper/1910.14389/) (2019)
- [The Univariate Marginal Distribution Algorithm Copes Well With Deception and Epistasis](https://mlsanity.com/paper/2007.08277/) (2020)
- [Level-Based Analysis of the Univariate Marginal Distribution Algorithm](https://mlsanity.com/paper/1807.10038/) (2018)
- [Theory of Estimation-of-Distribution Algorithms](https://mlsanity.com/paper/1806.05392/) (2018)
- [Upper Bounds on the Runtime of the Univariate Marginal Distribution Algorithm on OneMax](https://mlsanity.com/paper/1704.00026/) (2017)
- [From Understanding Genetic Drift to a Smart-Restart Parameter-less Compact Genetic Algorithm](https://mlsanity.com/paper/2004.07141/) (2020)
- [A Simplified Run Time Analysis of the Univariate Marginal Distribution Algorithm on LeadingOnes](https://mlsanity.com/paper/2004.04978/) (2020)

## Built on by (most cited first, 1 of 1)

- [Black-Box Combinatorial Optimization with Order-Invariant Reinforcement Learning](https://mlsanity.com/paper/2510.01824/) (2025)

## Abstract (arXiv)

We propose a general formulation of a univariate estimation-of-distribution algorithm (EDA). It naturally incorporates the three classic univariate EDAs compact genetic algorithm, univariate marginal distribution algorithm and population-based incremental learning as well as the max-min ant system with iteration-best update. Our unified description of the existing algorithms allows a unified analysis of these; we demonstrate this by providing an analysis of genetic drift that immediately gives the existing results proven separately for the four algorithms named above. Our general model also includes EDAs that are more efficient than the existing ones and these may not be difficult to find as we demonstrate for the OneMax and LeadingOnes benchmarks.