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.
@article{arxiv.1002.0745,
title = {Using CODEQ to Train Feed-forward Neural Networks},
author = {Mahamed G. H. Omran and Faisal al-Adwani},
journal= {arXiv preprint arXiv:1002.0745},
year = {2010}
}