In this paper, we improve bayesian optimization algorithms by introducing proportionate and rank-based assignment functions. A bayesianoptimization algorithm builds a bayesian network from a selected sub-population o...
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In this paper, we improve bayesian optimization algorithms by introducing proportionate and rank-based assignment functions. A bayesianoptimization algorithm builds a bayesian network from a selected sub-population of promising solutions, and this probabilistic model is employed to generate the offspring of the next generation. Our method assigns each solution a relative significance based on its fitness, and this information is used in building the bayesian network model. These assignment functions can improve the quality of the model without performing an explicit selection on the population. Numerical experiments demonstrate the effectiveness of this method compared to a conventional BOA. (C) 2007 Elsevier Inc. All rights reserved.
Estimation of distribution algorithms are considered to be a new class of evolutionary algorithms which are applied as an alternative to genetic algorithms. Such algorithms sample the new generation from a probabilist...
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Estimation of distribution algorithms are considered to be a new class of evolutionary algorithms which are applied as an alternative to genetic algorithms. Such algorithms sample the new generation from a probabilistic model of promising solutions. The search space of the optimization problem is improved by such probabilistic models. In the bayesianoptimization algorithm (BOA), the set of promising solutions forms a bayesian network and the new solutions are sampled from the built bayesian network. This paper proposes a novel real-coded stochastic BOA for continuous global optimization by utilizing a stochastic bayesian network. In the proposed algorithm, the new bayesian network takes advantage of using a stochastic structure (that there is a probability distribution function for each edge in the network) and the new generation is sampled from the stochastic structure. In order to generate a new solution, some new structure, and therefore a new bayesian network is sampled from the current stochastic structure and the new solution will be produced from the sampled bayesian network. Due to the stochastic structure used in the sampling phase, each sample can be generated based on a different structure. Therefore the different dependency structures can be preserved. Before the new generation is generated, the stochastic network's probability distributions are updated according to the fitness evaluation of the current generation. The proposed method is able to take advantage of using different dependency structures through the sampling phase just by using one stochastic structure. The experimental results reported in this paper show that the proposed algorithm increases the quality of the solutions on the general optimization benchmark problems.
Genetic algorithms (GAs) are a search and optimization technique based on the mechanism of evolution. Recently, another sort of population-based optimization method called Estimation of Distribution algorithms (EDAs) ...
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ISBN:
(纸本)1595930108
Genetic algorithms (GAs) are a search and optimization technique based on the mechanism of evolution. Recently, another sort of population-based optimization method called Estimation of Distribution algorithms (EDAs) have been proposed to solve the CA's defects. Although several comparison studies between GAs and EDAs have been made, little is known about differences of statistical features between them. In this paper, we propose new statistical indices which are based on the concepts of crossover and mutation, used in GAs, to analyze the behavior of the population based optimization techniques. We also show simple results of GAs and the bayesianoptimization Algorithm (BOA), a well-known Estimation of Distribution algorithms (EDAs).
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