一种基于遗传算法和LM算法的混合学习算法

针对遗传算法与神经网络结合方式中存在的早熟收敛、泛化能力弱等问题, 提出一种交替使用遗传算法和Levenberg Marquardt算法优化神经网络的混合学习算法（GALM算法）. 该算法先通过遗传算法粗调得到一组全局最优近似解, 再以该近似解为初值, 交替使用遗传算法和LM算法优化神经网络训练, 直至发现满意的网络参数. 实验结果表明, 新算法提高了网络的学习能力和收敛速度.

A Hybrid Algorithm Based on Genetic Algorithm and Levenberg-Marquardt

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 algorithmGALM, 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.