This paper describes a permutational-based Differential Evolution algorithm implemented in a wrapper scheme to find a feature subset to be applied in the construction of a near-optimal classifier. In this approach, the relevance of a feature chosen to build a better classifier is represented through its relative position in an integer-valued vector, and by using a permutational-based mutation operator, it is possible to create new feasible candidate solutions only. Furthermore, to provide a controlled diversity rate in the population, a straightforward repair-based recombination operator is utilized to evolve a population of candidate solutions. Unlike the other approaches in the existing literature using integer-valued vectors and requiring a predefined subset size, in this approach, this size is determined by an additional element included in the encoding scheme, allowing to find an adequate feature subset size to each specific dataset. Experimental results show that this approach is an effective way to create more accurate classifiers as they are compared with those obtained by other similar approaches.

本文介绍了一种基于排列的差分进化算法，该算法在包装器方案中实施，目的是找到要用于构造近优分类器的特征子集。在这种方法中，选择的用于构建更好分类器的特征的相关性通过其在整数值向量中的相对位置来表示，并且通过使用基于置换的变异算子，可以仅创建新的可行候选解。此外，为了在总体中提供受控的多样性率，使用了一种基于修复的简单重组算子来演化候选解决方案的总体。与现有文献中使用整数值向量并需要预定义子集大小的其他方法不同，在这种方法中，该大小由编码方案中包含的其他元素确定，从而可以为每个特定数据集找到足够的特征子集大小。实验结果表明，与通过其他类似方法获得的分类器相比，该方法是创建更准确分类器的有效方法。