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Estimation of Distribution Algorithms
2006Training Artificial Neural Networks (ANNs) is a very complex task with a high practical relevance in the field of supervised learning. In this chapter, the problem of training ANNs is faced with several Estimation of Distribution Algorithms (EDAs) with different features, exploring both continuous and discrete search spaces. These EDAs have been tested
Julio Madera, Bernabé Dorronsoro
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Estimation of Distribution Algorithms
2016Estimation of distribution algorithm (EDA) is a most successful paradigm of EAs. EDAs are derived by inspirations from evolutionary computation and machine learning. This chapter describes EDAs as well as several classical EDA implementations.
Ke-Lin Du, M. N. S. Swamy
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Explicit Exploration in Estimation of Distribution Algorithms
2013This work proposes an Estimation of Distribution Algorithm (EDA) that incorporates an explicit separation between the exploration stage and the exploitation stage. For each stage a probabilistic model is required. The proposed EDA uses a mixture of distributions in the exploration stage whereas a multivariate Gaussian distribution is used in the ...
Rogelio Salinas-Gutiérrez +3 more
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Estimation of Distribution Algorithms with Mutation
2005The Estimation of Distribution Algorithms are a class of evolutionary algorithms which adopt probabilistic models to reproduce the genetic information of the next generation, instead of conventional crossover and mutation operations. In this paper, we propose new EDAs which incorporate mutation operator to conventional EDAs in order to keep the ...
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Estimation of Distribution Algorithms
2015Estimation of distribution algorithms (EDA s) guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. However, EDAs are not only optimization techniques; besides the optimum or its approximation, EDAs provide practitioners with a series of probabilistic models that reveal a lot of ...
Martin Pelikan +2 more
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An Estimation of Distribution Algorithm for Motif Discovery
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008The problem of Transcription Factor Binding Sites identification or motif discovery is to identify the motif binding sites in the cis-regulatory regions of DNA sequences. The biological experiments are expensive and the problem is NP-hard computationally. We have proposed Estimation of Distribution Algorithm for Motif Discovery (EDAMD). We use Bayesian
Gang Li 0016 +3 more
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Copula Estimation of Distribution Algorithm with PMLE
2011 Seventh International Conference on Natural Computation, 2011Estimation of Distribution Algorithms (EDAs) have the task to estimate the distribution model of samples. Copula Estimation of Distribution Algorithms (cEDAs) introduce the copula theory into EDAs which divide the multivariate distribution estimation into two parts: the marginal distribution estimation and the estimation of the dependant structure of ...
Xiaodong Guo +3 more
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Pair-copula estimation of distribution algorithms
International Journal of Computing Science and Mathematics, 2013Summary: Copula theory provides a promising solution for the estimation of population probability in estimation distribution algorithms (EDAs), and more and more researchers pay attention to copula-EDAs. Most of the copula-EDAs researches are related to two variables case, in this paper, by taking advantage of the ability of pair-copula in high ...
Huimin Gao, Xiaoping Wang
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A Boltzmann based estimation of distribution algorithm
Information Sciences, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sergio Ivvan Valdez +2 more
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Symmetry in evolutionary and estimation of distribution algorithms
2013 IEEE Congress on Evolutionary Computation, 2013Symmetry has hitherto been studied piecemeal in a variety of evolutionary computation domains, with little consistency between the definitions. Here we provide formal definitions of symmetry that are consistent across the field of evolutionary computation.
Roberto Santana 0001 +2 more
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