This work describes the use of a weighted ensemble of neural network classifiers for adaptive learning. We train the neural networks by means of a quantum-inspired evolutionary algorithm (QIEA). The QIEA is also used to determine the best weights for each classifier belonging to the ensemble when a new block of data arrives. We show that the neuroevolutionary classifiers are able to learn the data set and to quickly respond to any drifts on the underlying data. We also compare the results reached by our model with an existing algorithm, Learn++.NSE, in two different nonstationary scenarios.
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