Ensemble learning is a system that combines a set of base learners to improve the performance in machine learning, where accuracy and diversity of base learners are two important factors. However, these two factors are usually contradictory. To address this problem, in this paper, we propose a novel ensemble learning algorithm based on fitness Euclidean-distance ratio differential evolution, to train the neural network ensemble. FEFERR_ELA employs a multimodal evolutionary algorithm that is capable of producing diverse solutions to search for optimal solutions corresponding to parameters of base learners, where each optimal solution leads to one trained model. A dynamic ensemble selection scheme is applied to select appropriate individuals for the ensemble. The proposed algorithm is evaluated on several benchmark problems and compared with some related ensemble learning models. The experimental results demonstrate that the proposed algorithm outperforms the related works and can produce the neural network ensembles with better generalization.

集成学习是一种系统，它结合了一组基础学习器以提高机器学习的性能，其中基础学习器的准确性和多样性是两个重要因素。但是，这两个因素通常是矛盾的。为了解决这个问题，本文提出了一种基于适应度欧氏距离比差分进化的新型集成学习算法，以训练神经网络集成。FEFERR_ELA采用了一种多模态进化算法，该算法能够产生多种解决方案，以搜索与基础学习者的参数相对应的最优解决方案，其中每个最优解决方案都可以生成一个训练过的模型。应用动态合奏选择方案为合奏选择合适的个人。该算法对几个基准问题进行了评估，并与一些相关的集成学习模型进行了比较。实验结果表明，所提算法优于相关工作，可以产生具有更好泛化能力的神经网络集成体。