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Efficient model building in competent genetic algorithms using DSM clustering

机译：使用DSM聚类的有效遗传算法中的有效模型构建

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摘要

Detecting multivariate interactions between the variables of a problem is a challenge in traditional genetic algorithms (GAs). This issue has been addressed in the literature as the linkage learning problem. It is widely acknowledged that the success of GA in solving any problem depends on the proper detection of multivariate interactions in the problem. Different approaches have thus been proposed to detect and represent such interactions. Estimation of distribution algorithms (EDAs) are amongst these approaches that have been successfully applied to a wide range of hard optimization problems. They build a model of the problem to detect multivariate interactions, but the model building process is often computationally intensive. In this paper, we propose a new clustering algorithm that turns pair-wise interactions in a dependency structure matrix (DSM) into an interaction model efficiently. The model building process is carried out before the evolutionary algorithm to save computational burden. The accurate interaction model obtained in this way is then used to perform an effective recombination of building blocks (BBs) in the GA. We applied the proposed approach to solve exemplar hard optimization problems with different types of linkages to show the effectiveness and efficiency of the proposed approach. Theoretical analysis and experiments showed that the building of an accurate model requires O(n log(n)) number of fitness evaluations. The comparison of the proposed approach with some existing algorithms revealed that the efficiency of the model building process is enhanced significantly.
机译：在传统的遗传算法（GA）中，检测问题的变量之间的多元交互是一项挑战。该问题在文献中已作为链接学习问题解决。众所周知，遗传算法在解决任何问题上的成功取决于对问题中多元相互作用的正确检测。因此已经提出了不同的方法来检测和表示这种相互作用。这些方法中，分布算法（EDA）的估计已成功地应用于各种硬优化问题。他们建立了一个问题模型来检测多元交互，但是模型的建立过程通常需要大量的计算。在本文中，我们提出了一种新的聚类算法，该算法可以将依赖结构矩阵（DSM）中的成对交互有效地转化为交互模型。为了节省计算负担，在进化算法之前进行了模型构建过程。然后，以这种方式获得的准确的交互模型将用于在GA中进行有效的结构单元（BB）重组。我们应用提出的方法来解决具有不同类型链接的示例性硬优化问题，以显示提出的方法的有效性和效率。理论分析和实验表明，建立准确的模型需要O（n log（n））个适合度评估。将该方法与现有算法进行比较，结果表明模型构建过程的效率得到了显着提高。

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