IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Neurocomputing
Robotics and Computer-Integrated Manufacturing
A new adaptive merging and growing algorithm for designing artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A meta-heuristic paradigm for solving the forward kinematics of 6-6 general parallel manipulator
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Direct search as unsupervised training algorithm for neural networks
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume II
Hybrid training of feed-forward neural networks with particle swarm optimization
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
A hybrid neural network model based reinforcement learning agent
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
PSO for reservoir computing optimization
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Expert Systems with Applications: An International Journal
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This paper introduces a methodology for neural network global optimization. The aim is the simultaneous optimization of multilayer perceptron (MLP) network weights and architectures, in order to generate topologies with few connections and high classification performance for any data sets. The approach combines the advantages of simulated annealing, tabu search and the backpropagation training algorithm in order to generate an automatic process for producing networks with high classification performance and low complexity. Experimental results obtained with four classification problems and one prediction problem has shown to be better than those obtained by the most commonly used optimization techniques