Metaheuristic Optimization Algorithms for Training Artificial Neural Networks
Ahmad Al Kawam, Nashat Mansour · 2012
Abstract—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 multilayer Perceptron network (MLP). We then evaluate the trained MLP‟s accuracy by applying four benchmark classification problems. Furthermore, the results obtained are compared to those attained using another competing meta-heuristic which is the Particle