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An Adaptive Differential Evolution with Learning Parameters According to Groups Defined by the Rank of Objective Values

机译：具有根据目标值等级定义的组的学习参数的自适应微分进化

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Differential Evolution (DE) has been successfully applied to various optimization problems. The performance of DE is affected by algorithm parameters such as a scaling factor F and a crossover rate CR. Many studies have been done to control the parameters adaptively. One of the most successful studies on controlling the parameters is JADE. In JADE, the values of each parameter axe generated according to one probability density function (PDF) which is learned by the values in success cases where the child is better than the parent. However, search performance might be improved by learning multiple PDFs for each parameter based on some characteristics of search points. In this study, search points are divided into plural groups according to the rank of their objective values and the PDFs are learned by parameter values in success cases for each group. The advantage of JADE with the group-based learning is shown by solving thirteen benchmark problems.
机译：差分进化（DE）已成功应用于各种优化问题。 DE的性能受算法参数的影响，例如缩放因子F和交叉速率CR。已经完成了许多研究以自适应地控制参数。关于控制参数的最成功的研究之一是玉。在玉器中，根据一个概率密度（PDF）生成的每个参数AX的值，该概率密度（PDF）是由孩子比父级更好的成功情况下的值。然而，通过基于搜索点的一些特征来学习每个参数的多个PDF，可以改善搜索性能。在本研究中，根据其客观值的等级将搜索点分成多个组，并且PDF在每个组的成功情况下通过参数值学习。通过解决十三个基准问题显示了基于组的学习的玉石的优势。

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