A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-Learning Context
Автор: Koffka Khan, Ashok Sahai
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 7 vol.4, 2012 года.
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Training neural networks is a complex task of great importance in the supervised learning field of research. We intend to show the superiority (time performance and quality of solution) of the new metaheuristic bat algorithm (BA) over other more “standard” algorithms in neural network training. In this work we tackle this problem with five algorithms, and try to over a set of results that could hopefully foster future comparisons by using a standard dataset (Proben1: selected benchmark composed of problems arising in the field of Medicine) and presentation of the results. We have selected two gradient descent algorithms: Back propagation and Levenberg-Marquardt, and three population based heuristic: Bat Algorithm, Genetic Algorithm, and Particle Swarm Optimization. Our conclusions clearly establish the advantages of the new metaheuristic bat algorithm over the other algorithms in the context of eLearning.
Neural networks, supervised learning, metaheuristic, bat algorithm, backpropagation, Levenberg-Marquardt, genetic algorithm, particle swarm optimization
Короткий адрес: https://sciup.org/15010276
IDR: 15010276
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