Results 211 to 220 of about 173,639,131 (247)
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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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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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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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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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Distributed algorithm for SDP state estimation
2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2013Smart Grid State Estimation (SE) aims at providing robust and accurate system state estimate for subsequent control operations to accommodate the disturbance of highly intermittent components. Conventional SE for AC Power Grid formulates the estimation process as a non-convex optimization problem, which may reach a local optimal and stop. To compensate
Yang Weng +3 more
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Variable Transformations in Estimation of Distribution Algorithms
2012In this paper we address model selection in Estimation of Distribution Algorithms (EDAs) based on variables trasformations. Instead of the classic approach based on the choice of a statistical model able to represent the interactions among the variables in the problem, we propose to learn a transformation of the variables before the estimation of the ...
CUCCI, DAVIDE ANTONIO +2 more
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