An introduction and survey of estimation of distribution algorithms

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. This explicit use of probabilistic models in optimization offers some significant...

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Bibliographic Details
Published in:Swarm and evolutionary computation Vol. 1; no. 3; pp. 111 - 128
Main Authors: Hauschild, Mark, Pelikan, Martin
Format: Journal Article
Language:English
Published: Elsevier B.V 01.09.2011
Subjects:
Decomposable problems
Estimation of distribution algorithms
Evolutionary computation
Model building
Probabilistic models
Stochastic optimization
Estimation of distribution algorithms
Decomposable problems
Probabilistic models
Model building
Stochastic optimization
Evolutionary computation
ISSN:2210-6502
Online Access:Get full text
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Summary:Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. This explicit use of probabilistic models in optimization offers some significant advantages over other types of metaheuristics. This paper discusses these advantages and outlines many of the different types of EDAs. In addition, some of the most powerful efficiency enhancement techniques applied to EDAs are discussed and some of the key theoretical results relevant to EDAs are outlined. ► Introduces and describes many Estimation of Distribution Algorithms (EDAs). ► Targets a broad audience and strongly motivates the use of EDAs. ► Covers many algorithms not mentioned in previous surveys on EDAs. ► Also covers efficiency enhancements particular to EDAs.
ISSN:2210-6502
DOI:10.1016/j.swevo.2011.08.003