Triangulation of Bayesian networks with recursive estimation of distribution algorithms

Romero, Txomin; Larraòaga, Pedro

doi:10.1016/j.ijar.2008.09.002

Cited by 25 publications

(15 citation statements)

References 20 publications

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“…Regarding specific-EDA parameters, the values suggested by their respective authors have been used. Romero et al [43] suggest executing REDA with fast execution EDAs since they will be run repeatedly, thus we use UMDA and MIMIC, as the authors do in their experiments. On the other hand, Tsutsui [51] suggests setting the B ratio constant to 0.0002 for EHBSA and NHBSA.…”

Section: Experiments Setupmentioning

confidence: 99%

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

et al. 2012

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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.

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Section: Experiments Setupmentioning

confidence: 99%

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

et al. 2012

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“…Some classical examples of RK based EDAs are REDA [15], EGNA ee & UMDA c [10]. REDA uses the triangulation of Bayesian network approach and focuses on model efficiency by modelling subset nodes of a problem.…”

Section: Introductionmentioning

confidence: 99%

RK-EDA: A Novel Random Key Based Estimation of Distribution Algorithm

Ayodele

McCall

Regnier-Coudert

2016

Parallel Problem Solving From Nature – PPSN XIV

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“…Permutation problems [3,8,84,86,87] and problems with unitation constraints [70,101] are two examples. A challenging topic in the application of EDAs to these problems is the conception of probabilistic models able to effectively represent constraints.…”

Section: Highly Complicated Mixed Constrained and Other Difficult mentioning

confidence: 99%

Research topics in discrete estimation of distribution algorithms based on factorizations

2008

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In this paper, we identify a number of topics relevant for the improvement and development of discrete estimation of distribution algorithms. Focusing on the role of probability distributions and factorizations in estimation of distribution algorithms, we present a survey of current challenges where further research must provide answers that extend the potential and applicability of these algorithms. In each case we state the research topic and elaborate on the reasons that make it relevant for estimation of distribution algorithms. In some cases current work or possible alternatives for the solution of the problem are discussed.

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Triangulation of Bayesian networks with recursive estimation of distribution algorithms

Cited by 25 publications

References 20 publications

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

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

RK-EDA: A Novel Random Key Based Estimation of Distribution Algorithm

Research topics in discrete estimation of distribution algorithms based on factorizations

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