International Journal of Computer and Information Technology (IJCIT)
Training neural networks is a complex task that is important for supervised learning. A few metaheuristic optimization techniques have been applied to increase the effectiveness of the training process. The Cuckoo Search (CS) algorithm is a recently developed meta-heuristic optimization algorithm which is suitable for solving optimization problems. In this paper, Cuckoo search is implemented in training a feed forward Multi-Layer Perceptron network (MLP). The authors then evaluate the trained MLPs accuracy by applying four benchmark classification problems.