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A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems

PROGRESS IN ARTIFICIAL INTELLIGENCE 2012年第1期1卷 103-117页

作者： Ceberio, Josu Irurozki, Ekhine Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Comp Sci & Artificial Intelligence Dept Intelligent Syst Grp Donostia San Sebastian Spain

Estimation of distribution algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies between the variables of the problem, these algorithms have been applied to a wide set of academic and real-world optimization problems, achieving competitive results in most scenarios. Nevertheless, there are some optimization problems, whose solutions can be naturally represented as permutations, for which EDAs have not been extensively developed. Although some work has been carried out in this direction, most of the approaches are adaptations of EDAs designed for problems based on integer or real domains, and only a few algorithms have been specifically designed to deal with permutation-based problems. In order to set the basis for a development of EDAs in permutation-based problems similar to that which occurred in other optimization fields (integer and real-value problems), in this paper we carry out a thorough review of state-of-the-art EDAs applied to permutation-based problems. Furthermore, we provide some ideas on probabilistic modeling over permutation spaces that could inspire the researchers of EDAs to design new approaches for these kinds of problems.

关键词： Evolutionary computation Estimation of distribution algorithms permutation-based optimization problems Probabilistic permutation modelling

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