Neural network learning with global heuristic search

Ivan Jordanov, A. Georgieva

Research output: Contribution to journalArticlepeer-review


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
Original languageEnglish
Pages (from-to)937-942
Number of pages6
JournalIEEE Transactions on Neural Networks
Issue number3
Publication statusPublished - May 2007


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