  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.
