General Univariate Estimation-of-Distribution Algorithms

TL;DR

This paper introduces a unified framework for univariate EDAs, encompassing several classic algorithms and analyzing their genetic drift, while also proposing more efficient variants for benchmark problems.

Contribution

It provides a unified formulation of univariate EDAs, enabling comprehensive analysis and the development of more efficient algorithms for standard benchmarks.

Findings

Unified analysis of classic EDAs and ant systems.

Demonstrated more efficient EDAs for OneMax and LeadingOnes.

Provided insights into genetic drift in univariate EDAs.

Abstract

We propose a general formulation of a univariate estimation-of-distribution algorithm (EDA). It naturally incorporates the three classic univariate EDAs \emph{compact genetic algorithm}, \emph{univariate marginal distribution algorithm} and \emph{population-based incremental learning} as well as the \emph{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.