An introduction and survey of estimation of distribution algorithms

Hauschild, Mark W.; Pelikán, Martin

doi:10.1016/j.swevo.2011.08.003

Cited by 411 publications

(232 citation statements)

References 99 publications

(128 reference statements)

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“…The ability to model the features of more promising solutions is a major attribute that differentiates them from most other EAs [7]. They benefit from the use of machine learning techniques, which makes them better at solving certain categories of larger and more difficult problems [12].…”

Section: Introductionmentioning

confidence: 99%

“…Also, variability in the values that capture the same priority across solutions of a population limits the information captured by the probabilistic model. They therefore struggle to produce competitive results [7]. Models that are more specific to permutations such as histogram models [16], [17], permutation distribution models [4], [6], [5] and factoradics [14] have shown better performances.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Introductionmentioning

confidence: 99%

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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“…In general, those new individuals have the same proprieties of the best solutions of the precedent generation. The algorithm runs many generations according to the steps described above until a criterion stop is achieved [2].…”

Section: Estimation Of Distribution Algorithmsmentioning

confidence: 99%

“…This paper propose, a Copulabased Estimation of Distribution Algorithm. CEDA belongs to the class of Estimation of Distribution Algorithms (EDA) [2], which is itself a class of Evolutionary Algorithms (EA) [3] usually used to solve multiobjective problems. In contrast to EA where new solutions are generated using an implicit distribution defined by one or more variation operator (mutation, crossover), EDA uses an explicit probability distribution model to characterize the interactions between the solutions.…”

Section: Introductionmentioning

confidence: 99%

A Posteriori Pareto Front Diversification Using a Copula-Based Estimation of Distribution Algorithm

Cheriet¹,

Cherif²

2015

ijacsa

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Abstract-We propose CEDA, a Copula-based Estimation of Distribution Algorithm, to increase the size, achieve high diversity and convergence of optimal solutions for a multiobjective optimization problem. The algorithm exploits the statistical properties of Copulas to produce new solutions from the existing ones through the estimation of their distribution. CEDA starts by taking initial solutions provided by any MOEA (Multi Objective Evolutionary Algorithm), construct Copulas to estimate their distribution, and uses the constructed Copulas to generate new solutions. This design saves CEDA the need of running an MOEA every time alternative solutions are requested by a Decision Maker when the found solutions are not satisfactory. CEDA was tested on a set of benchmark problems traditionally used by the community, namely UF1, UF2, ..., UF10 and CF1, CF2, ..., CF10. CEDA used along with SPEA2 and NSGA2 as two examples of MOEA thus resulting in two variants CEDA-SPEA2 and CEDA-NSGA2 and compare them with SPEA2 and NSGA2. The results of The experiments show that, with both variants of CEDA, new solutions can be generated in a significantly smaller without compromising quality compared to those found SPEA2 and NSGA2.

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“…EDA _PR ALGORITHM Recently, estimation of distribution algorithm (EDA) has attracted much attention by evolutionary algorithm researchers due to their search abilities. Estimation of distribution algorithm is one of the most popular evolutionary algorithms which estimate the probability distribution associated with the selected individuals and sample this distribution to create the next population in recent years [6][7]. Different from the GA that produces offspring through crossover and mutation operations, estimation of distribution algorithm (EDA) does it by sampling according to a probability model which has a great effect on the performances of EDA.…”

Section: Introductionmentioning

confidence: 99%

A decision support system with EDA_PR algorithm for the hot rolling scheduling

Zhang¹,

Ma²

2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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Abstract-This paper presents a hybrid algorithm for the hot rolling scheduling problem, which is derived from the actual steel production, and some features make the solution methodology more difficult. The hybrid strategy is based on the solution construction mechanism of estimation of distribution algorithm (EDA) with path relinking (PR), an evolutionary method, which results in a novel approach that we call EDA_PR. Moreover, a decision support system in which the algorithm has been embedded for the hot rolling scheduling is designed. The computational experiments show that the EDA_PR method has more potential for improvement to solve the hot rolling scheduling problem compared with the manual scheduling method.

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An introduction and survey of estimation of distribution algorithms

Cited by 411 publications

References 99 publications

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

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

A Posteriori Pareto Front Diversification Using a Copula-Based Estimation of Distribution Algorithm

A decision support system with EDA_PR algorithm for the hot rolling scheduling

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