Title: A Hybrid Algorithm Based on Genetic Algorithm and Levenberg-Marquardt
Abstract: In order to overcome the insufficiencies of premature convergence and weak extensive ability in the combination of Genetic Algorithm and Artificial Neural Networks,we proposed a new hybrid study algorithmGALM, which uses the Genetic Algorithm and Levenberg-Marquardt in turn to optimize the neural network.This algorithm mainly includes two stages: First a group of solutions were obtained which approximate the global optimum through cursorily adjusting the genetic algorithm.Then these approximate solutions were taken as the initial values,the GA and LM algorithms were used to optimize the neural network training in turn until the satisfactory network parameters were found.Finally we compared the GALM algorithm with other relevant algorithms through experimentation.The results indicate that our algorithm can effectively overcome the problem about falling into the local optimal solutions,and remarkably improves the network learning capability and the convergence rate.
Publication Year: 2008
Publication Date: 2008-01-01
Language: en
Type: article
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Cited By Count: 2
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