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Maximum spanning tree based linkage learner

机译：基于最大生成树的链接学习器

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Linkage learning in evolutionary algorithms is identifying the structure of the dependencies between variables of a problem in order to find the optimum solution of the problem. It is a necessary process for optimizing the hard problems that can not be optimized randomly via common recombination operators of simple genetic algorithm. This paper presents a simple yet effective linkage learner that works based on graph theory. A graph is used as the structure to keep the pairwise dependencies between variables of the problem. We call this graph 'the underlying dependency graph of the problem' (UDGP). Maximum spanning tree (MST) of the UDGP is then found. It is shown that MST contains all the necessary linkages if the dependency graph is built upon sufficient population. In this approach, pairwise dependencies that are mutual information between each pair of variables, are used to find linkage information. The proposed approach has the advantage of being capable of learning the linkage without the need for the costly fit-to-data evaluations of model search. It is parameter free and the algorithm description is straight forward. The proposed technique is tested on several benchmark problems and it is shown to be able to compete with similar approaches. Based on the experimental results it can successfully find the linkage groups in a polynomial number of fitness evaluations.
机译：进化算法中的链接学习是识别问题变量之间的依存关系结构，以便找到问题的最佳解决方案。这是优化难以通过普通遗传算法的常见重组算子随机优化的难题的必要过程。本文提出了一个简单而有效的基于图论的链接学习器。图用作保持问题变量之间成对依存关系的结构。我们将此图称为“问题的基础依赖性图”（UDGP）。然后找到UDGP的最大生成树（MST）。结果表明，如果依赖关系图建立在足够的总体上，则MST包含所有必要的链接。在这种方法中，使用成对依赖关系（即每对变量之间的互信息）来查找链接信息。所提出的方法的优点是能够学习链接，而无需对模型搜索进行昂贵的数据拟合评估。它没有参数，算法描述也很简单。所提出的技术已经在几个基准问题上进行了测试，并且证明可以与类似的方法竞争。根据实验结果，它可以成功地在多项式适应性评估中找到链接组。

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