A Parallel Island Model for Estimation of Distribution Algorithms
A Parallel Island Model for Estimation of Distribution Algorithms
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DOI：
10.1007/3-540-32494-1_7
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发表时间：
2006
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影响因子：
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通讯作者：
Julio Madera;E. Alba;A. Ochoa
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文献类型：
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作者：
Julio Madera;E. Alba;A. Ochoa
在这项工作中，我们解决了一种称为分布估计算法（EDAs）的进化算法（ea）的并行化。在对eda的潜在并行方案类型进行初步讨论之后，我们继续设计一个分布式孤岛版本（dEDA），旨在提高顺序算法在评估次数方面的数值效率。在对几个众所周知的离散和连续测试问题评估了这样的dEDA之后，我们得出结论，从数值的角度来看，我们的模型明显优于现有的集中式方法，并且由于其适合物理并行性，大大加快了搜索速度。
In this work we address the parallelization of the kind of Evolutionary Algorithms (EAs) known as Estimation of Distribution Algorithms (EDAs). After an initial discussion on the types of potentially parallel schemes for EDAs, we proceed to design a distributed island version (dEDA), aimed at improving the numerical efficiency of the sequential algorithm in terms of the number of evaluations. After evaluating such a dEDA on several well-known discrete and continuous test problems, we conclude that our model clearly outperforms existing centralized approaches from a numerical point of view, as well as speeding up the search considerably, thanks to its suitability for physical parallelism.