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Space Complexity of Estimation of Distribution Algorithms
Evolutionary Computation, 2005In this paper, we investigate the space complexity of the Estimation of Distribution Algorithms (EDAs), a class of sampling-based variants of the genetic algorithm. By analyzing the nature of EDAs, we identify criteria that characterize the space complexity of two typical implementation schemes of EDAs, the factorized distribution algorithm and ...
Yong Gao 0001, Joseph C. Culberson
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Estimating Distributions in Genetic Algorithms
2003The canonical operators of genetic algorithms, i.e., mutation and crossover, have nondeterministic effects on the population.They use information from only one or two fit individuals and risk deforming the chromosomes of fit individuals and cause an interruption in the progression.
Onur Dikmen +2 more
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Applied Soft Computing, 2011
Abstract: In the traditional estimation of distribution algorithms (EDAs), all the variables of candidate individuals are perturbed through sampling from a probability distribution of promising individuals. However, it may be unnecessary for the EDAs to perturb all variables of candidate individuals at each generation.
Helong Li, Yi Hong 0002, Sam Kwong
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Abstract: In the traditional estimation of distribution algorithms (EDAs), all the variables of candidate individuals are perturbed through sampling from a probability distribution of promising individuals. However, it may be unnecessary for the EDAs to perturb all variables of candidate individuals at each generation.
Helong Li, Yi Hong 0002, Sam Kwong
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An Introduction and Survey of Estimation of Distribution Algorithms
Swarm and Evolutionary Computation, 2011Abstract Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. This explicit use of probabilistic models in optimization offers some significant advantages over other types of ...
Martin Pelikán, Mark Hauschild
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Drift and Scaling in Estimation of Distribution Algorithms
Evolutionary Computation, 2005This 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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On the Convergence of a Class of Estimation of Distribution Algorithms
IEEE Transactions on Evolutionary Computation, 2004We investigate the global convergence of estimation of distribution algorithms (EDAs). In EDAs, the distribution is estimated from a set of selected elements, i.e., the parent set, and then the estimated distribution model is used to generate new elements.
Qingfu Zhang
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Estimation of Distribution Algorithms with Kikuchi Approximations
Evolutionary Computation, 2005The 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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On the limits of effectiveness in estimation of distribution algorithms
2011 IEEE Congress of Evolutionary Computation (CEC), 2011Which problems a search algorithm can effectively solve is a fundamental issue that plays a key role in understanding and developing algorithms. In order to study the ability limit of estimation of distribution algorithms (EDAs), this paper experimentally tests three different EDA implementations on a sequence of additively decomposable functions (ADFs)
Carlos Echegoyen +4 more
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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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