Solving the N-bit parity problem using neural networks
Neural Networks
Ensemble learning via negative correlation
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving artificial neural network ensembles
IEEE Computational Intelligence Magazine
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Learning polynomial feedforward neural networks by genetic programming and backpropagation
IEEE Transactions on Neural Networks
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Artificial neural networks (ANNs) have been successfully applied to many areas due to its powerful ability both for classification and regression problems For some difficult problems, ANN ensemble classifiers are considered, instead of a single ANN classifier In the previous study, the authors presented the systematic trajectory search algorithm (STSA) to train the ANN The STSA utilizes the orthogonal array (OA) to uniformly generate the initial population to globally explore the solution space, and then applies a novel trajectory search method to exploit the promising areas thoroughly In this paper, an evolutionary constructing algorithm, called the ESTSA, of the ANN ensemble is proposed Based on the STSA, the authors introduce a penalty term to the error function in order to guarantee the diversity of ensemble members The performance of the proposed algorithm is evaluated by applying it to train a class of feedforward neural networks to solve the large n-bit parity problems By comparing with the previous studies, the experimental results revealed that the neural network ensemble classifiers trained by the ESTSA have very good classification ability.