Using CODEQ to Train Feed-forward Neural Networks

Mahamed G. H. Omran; Faisal al-Adwani

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arXiv · 1002.0745

Using CODEQ to Train Feed-forward Neural Networks

Mahamed G. H. Omran·Faisal al-Adwani

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Abstract

CODEQ is a new, population-based meta-heuristic algorithm that is a hybrid of concepts from chaotic search, opposition-based learning, differential evolution and quantum mechanics. CODEQ has successfully been used to solve different types of problems (e.g. constrained, integer-programming, engineering) with excellent results. In this paper, CODEQ is used to train feed-forward neural networks. The proposed method is compared with particle swarm optimization and differential evolution algorithms on three data sets with encouraging results.

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BibTeX RIS

Mahamed G. H. Omran, Faisal al-Adwani. 2010-02-03. Using CODEQ to Train Feed-forward Neural Networks. https://arxiv.org/abs/1002.0745

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