Theory of Estimation-of-Distribution Algorithms

TL;DR

This paper reviews recent theoretical advances in estimation-of-distribution algorithms (EDAs), focusing on runtime analysis of simple univariate EDAs and their application to benchmark functions.

Contribution

It provides an up-to-date overview of the theoretical understanding of EDAs, highlighting recent results and open problems in the field.

Findings

Progress in runtime analysis of simple univariate EDAs

Identification of benchmark functions for EDA analysis

Discussion of open problems and future research directions

Abstract

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.