期刊
SWARM AND EVOLUTIONARY COMPUTATION
卷 1, 期 3, 页码 111-128出版社
ELSEVIER
DOI: 10.1016/j.swevo.2011.08.003
关键词
Stochastic optimization; Estimation of distribution algorithms; Probabilistic models; Model building; Decomposable problems; Evolutionary computation
资金
- National Science Foundation under CAREER grant [ECS-0547013]
- University of Missouri in St. Louis - Information Technology Services
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1115352] Funding Source: National Science Foundation
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. (C) 2011 Elsevier B.V. All rights reserved.
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