| Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks — A Case Study for the Optimal Ordering of Tables |
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| 引用本文： | Concha Bielza,Juan A. Fernndez del Pozo,Pedro Larranaga.Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks — A Case Study for the Optimal Ordering of Tables[J].计算机科学技术学报,2013,28(4):720-731. |
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| 作者姓名： | Concha Bielza Juan A. Fernndez del Pozo Pedro Larranaga |
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| 作者单位： | Computational Intelligence Group, Department of Artificial Intelligence,Technical University of Madrid |
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| 基金项目： | partially supported by the Spanish Ministry of Economy and Competitiveness under Grant No.TIN2010-20900-C04-04 and Cajal Blue Brain |
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| 摘 要： | Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation. There are two main approaches to parameter setting: parameter tuning and parameter control. In this paper, we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation. The nodes of this Bayesian network are genetic algorithm parameters to be controlled. Its structure captures probabilistic conditional (in)dependence relationships between the parameters. They are learned from the best individuals, i.e., the best configurations of the genetic algorithm. Individuals are evaluated by running the genetic algorithm for the respective parameter configuration. Since all these runs are time-consuming tasks, each genetic algorithm uses a small-sized population and is stopped before convergence. In this way promising individuals should not be lost. Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time. Moreover, our approach can cope with as yet unsolved high-dimensional problems.
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| 关 键 词： | genetic algorithm estimation of distribution algorithm parameter control parameter setting Bayesian net-work |
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