This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the best individuals from each population in the same proportion and combining them to constitute a new initial population. At this point the main loop of an evolutionary algorithm is applied to the new population. The results show that our proposal considerably improves both the efficiency of previous methodologies and also, significantly, their efficacy in most of the data sets. We have carried out our experimentation on twelve data sets from the UCI repository and two complex real-world problems which differ in their number of instances, features and classes.

翻译：本文提出了一种在进化过程初期增加多样性的方法。该方法首先创建两个具有不同参数设置的初始种群，经过少量代数的进化后，以相同比例从每个种群中选择最优个体，并将它们组合成新的初始种群。随后，进化算法的主循环应用于该新种群。实验结果表明，本方案显著提升了先前方法的效率，并且在大多数数据集上其有效性也有明显提高。我们在UCI数据库的十二个数据集以及两个复杂度高、在实例数量、特征和类别上存在差异的真实世界问题上进行了实验验证。