高效寻优的经验遗传算法

An Efficient Global Optimization Algorithm:Empirical Genetic Algorithm

为了寻求高效的寻优方法,本文提出经验遗传算法,用神经网络模型经验地预测每代种群个体的适应度,从而减少对问题直接求解的次数,提高遗传算法的计算效率．通过对6个经典测试函数的数值计算分析,结果验证了本文所提的算法的有效性,而且能降低计算量．

It is necessary to obtain corresponcling solutions to evaluating the fitness of all individuals of every generation of the population and to analyze the solutions by using Genetic Algorithm. When the scale of problem is large, the calculation of genetic algorithm will be so enormous that it ean not be used in practice. However, a new method called empirical genetic algorithm is proposed in the paper. It decrease the number to analyze the solution and increase the efficiency of the genetic algorithm, in which the fitness of most individuals of every generation of the population are estimated by the empirical Neural Network. The calculation results from six classical test functions show that the method is efficient.