遗传算法
基于群体的增量学习
计算机科学
趋同(经济学)
人工神经网络
文化算法
元优化
算法
数学优化
功能(生物学)
人工智能
机器学习
数学
经济增长
进化生物学
生物
经济
作者
Akbar A. Javadi,Raziyeh Farmani,T.P. Tan
标识
DOI:10.1016/j.aei.2005.07.003
摘要
Application of genetic algorithms to optimization of complex problems can lead to a substantial computational effort as a result of the repeated evaluation of the objective function(s) and the population-based nature of the search. This is often the case where the objective function evaluation is costly, for example, when the value is obtained following computationally expensive system simulations. Sometimes a substantially large number of generations might be required to find optimum value of the objective function. Furthermore, in some cases, genetic algorithm can face convergence problems. In this paper, a hybrid optimization algorithm is presented which is based on a combination of the neural network and the genetic algorithm. In the proposed algorithm, a back-propagation neural network is used to improve the convergence of the genetic algorithm in search for global optimum. The efficiency of the proposed computational methodology is illustrated by application to a number of test cases. The results show that, in the proposed hybrid method, the integration of the neural network in the genetic algorithm procedure can accelerate the convergence of the genetic algorithm significantly and improve the quality of solution.
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