Linkage Identification by Fitness Difference Clustering
Linkage Identification by Fitness Difference Clustering
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DOI：
10.1162/evco.2006.14.4.383
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发表时间：
2006-12
影响因子：
6.8
通讯作者：
Miwako Tsuji;M. Munetomo;K. Akama
中科院分区：
文献类型：
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作者：
Miwako Tsuji;M. Munetomo;K. Akama
遗传算法执行交叉有效的连锁集的变量集紧密相连，形成积木被确定。已经提出了几种方法来检测的连锁集。扰动方法（PM）通过基因值的扰动来研究适应度差异，分布估计算法（EDA）估计有希望的字符串的分布。在本文中，我们提出了一种新的方法，结合两者，它通过估计分布的字符串聚类，根据适应度差异检测变量的依赖关系。所提出的算法，称为依赖检测的分布来自适应度差异（D5），可以检测一类函数的依赖性，这是很难的EDA，并需要更少的计算成本比PM。
Genetic Algorithms perform crossovers effectively when linkage sets sets of variables tightly linked to form building blocks are identified. Several methods have been proposed to detect the linkage sets. Perturbation methods (PMs) investigate fitness differences by perturbations of gene values and Estimation of distribution algorithms (EDAs) estimate the distribution of promising strings. In this paper, we propose a novel approach combining both of them, which detects dependencies of variables by estimating the distribution of strings clustered according to fitness differences. The proposed algorithm, called the Dependency Detection for Distribution Derived from fitness Differences (D5), can detect dependencies of a class of functions that are difficult for EDAs, and requires less computational cost than PMs.