This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantum-inspired evolutionary models to automatically configure the ensemble members and combine their output. The quantum-inspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour.

这项工作提出了一种新的神经进化模型，称为NEVE（神经进化合奏），它基于多层感知器（MLP）神经网络的集成，用于在非平稳环境中进行学习。NEVE利用受量子启发的进化模型自动配置集合成员并组合其输出。受量子启发的进化模型为每个MLP网络确定最合适的拓扑，选择最相关的输入变量，确定神经网络权重并计算每个集合成员的投票权重。开发了四种不同的NEVE方法，改变了检测和处理概念漂移的机制，包括主动漂移检测方法。在真实和人工数据集中评估了建议的模型，将获得的结果与文献中的其他合并模型进行比较。结果表明，在大多数情况下，NEVE的精度较高，并且使用某种用于漂移检测的机制可获得最佳配置。这些结果表明，对于数据集的行为会突然发生变化的情况，神经进化集成方法是一种可靠的选择。