摘要
利用演化算法的自适应、自组织、自学习的特性，设计了遗传程序设计与遗传算法相嵌套的常微分方程组混合演化建模算法，以遗传程序设计优化模型结构，以遗传算法优化模型参数，首次实现了常微分方程组建模过程自动化并可进行有效的预测．数值实验表明：采用这种算法能在较短的运行时间和较小的演化代数内搜索到多个较优的常微分方程组模型。
Based on the properties of self adaptation, self organization and self learning of evolutionary algorithms, a hybrid evolutionary modeling algorithm is proposed in this paper to solve the modeling problem of ordinary differential equations (ODEs). Its main idea is to embed a genetic algorithm (GA) into genetic programming (GP) where GP is employed to optimize the structure of a model, while a GA is employed to optimize the parameters of the model. It has taken the first step to making the modeling process of ODEs done automatically as well as giving reliable predictions. The numerical experiments show that multiple highly precise ODEs models can be searched out in a reasonable time and within fewer generations, and their predicted values surprisingly coincide with the exact solutions of the known ODEs using this algorithm.
出处
《计算机学报》
EI
CSCD
北大核心
1999年第8期871-876,共6页
Chinese Journal of Computers
基金
国家自然科学基金
国家八六三高技术研究发展计划
关键词
演化建模
常微分方程组
遗传算法
遗传程序设计
Evolutionary modeling, system of ordinary differential equations, genetic algorithm, genetic programming.
