Results 241 to 250 of about 787,704 (278)

Estimation of Distribution Algorithms

2016
Estimation 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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Estimation of Distribution Algorithms

2015
Estimation 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, Mark W. Hauschild, Fernando G. Lobo   +2 more
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Estimation of distribution algorithms

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011
This paper focuses on the analysis of estimation of distribution algorithms (EDAs) software. The important role played by EDAs implementations in the usability and range of applications of these algorithms is considered. A survey of available EDA software is presented, and classifications based on the class of programming languages and design ...
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Estimation of Distribution Algorithms

2006
Training 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 with Kikuchi Approximations

Evolutionary Computation, 2005
The question of finding feasible ways for estimating probability distributions is one of the main challenges for Estimation of Distribution Algorithms (EDAs). To estimate the distribution of the selected solutions, EDAs use factorizations constructed according to graphical models. The class of factorizations that can be obtained from these probability
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Pair-copula estimation of distribution algorithms

International Journal of Computing Science and Mathematics, 2013
Summary: 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 ...
Gao, Huimin, Wang, Xiaoping
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Dictionary based estimation of distribution algorithms

2007 International Symposium on Communications and Information Technologies, 2007
This paper proposes a new algorithm in the field of estimation of distribution algorithms. The proposed algorithm combines a data compression algorithm to extract a model into the dictionary. This dictionary is used as a part of the generator to generate the better next generation of population.
null Chalermsub Sangkavichitr, null Prabhas Chongstitvattana   +1 more
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Estimation of particle swarm distribution algorithms

Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009
This paper presents a framework of estimation of particle swarm distribution algorithms (EPSDAs). The aim lies in effectively combining particle swarm optimization (PSO) with estimation of distribution algorithms (EDAs) without losing on their unique features.
Chang Wook Ahn, Hyun-Tae Kim
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Drift and Scaling in Estimation of Distribution Algorithms

Evolutionary Computation, 2005
This paper considers a phenomenon in Estimation of Distribution Algorithms (EDA) analogous to drift in population genetic dynamics. Finite population sampling in selection results in fluctuations which get reinforced when the probability model is updated.
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Chaos elitism estimation of distribution algorithm

Fifth International Conference on Intelligent Control and Information Processing, 2014
Estimation of distribution algorithm (EDA) is a kind of EAs, which is based on the technique of probabilistic model and sampling. This paper presents a chaos elitism EDA to improve the performance of traditional EDA to solve high dimensional optimization problems. The famous elitism strategy is introduced to maintain a good convergent performance.
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