Hybrid Metaheuristics for Global Optimization: A Comparative Study
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Intelligent hybrid system for pattern recognition and classification
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Single-Layer Neural Net Competes with Multi-layer Neural Net
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
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IEEE Transactions on Neural Networks
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Performance Evaluation
Expert Systems with Applications: An International Journal
An overview of the use of neural networks for data mining tasks
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
The effect of lateral inhibitory connections in spatial architecture neural network
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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A novel hybrid global optimization (GO) algorithm applied for feedforward neural networks (NNs) supervised learning is investigated. The network weights are determined by minimizing the traditional mean square error function. The optimization technique, called LPtau NM, combines a novel global heuristic search based on LPtau low-discrepancy sequences of points, and a simplex local search. The proposed method is initially tested on multimodal mathematical functions and subsequently applied for training moderate size NNs for solving popular benchmark problems. Finally, the results are analyzed, discussed, and compared with such as from backpropagation (BP) (Levenberg-Marquardt) and differential evolution methods