Theory of estimation-of-distribution algorithms

Martin S. Krejca*, Carsten Witt

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

Estimation-of-distribution algorithms (EDAs) are general metaheuristics used in optimization that represent a more recent alternative to classical approaches such as 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, significant progress has been made in the theoretical understanding of EDAs. This chapter 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.

Original languageEnglish
Title of host publicationNatural Computing Series
EditorsB. Doerr , F. Neumann
PublisherSpringer
Publication date1 Jan 2020
Pages405-442
ISBN (Print)978-3-030-29413-7
DOIs
Publication statusPublished - 1 Jan 2020
SeriesNatural Computing Series
ISSN1619-7127

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