An introduction and survey of estimation of distribution algorithms

  title={An introduction and survey of estimation of distribution algorithms},
  author={Mark Hauschild and Martin Pelikan},
  journal={Swarm and 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 probablistic 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… CONTINUE READING
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Publications referenced by this paper.
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D. E. Goldberg
Kluwer. • 2002
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