An introduction and survey of estimation of distribution algorithms

作者：Hauschild Mark^*; Pelikan Martin

来源：Swarm and Evolutionary Computation, 2011, 1(3): 111-128.

DOI：10.1016/j.swevo.2011.08.003

• Stochastic optimization
• Estimation of distribution algorithms
• Probabilistic models
• Model building
• Decomposable problems
• Evolutionary computation

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.

• 出版日期2011-9