摘要
基因表达式编程是一种新型的自适应演化算法，它是在继承和发展遗传算法和遗传编程优点的基础上发展起来的知识发现新技术．笔者介绍了GEP的发展现状与关键技术，设计了逆淘汰策略和无树解码方式的改进方案，旨在维持种群多样性和提高算法效率，最后将改进方法应用与一元和多元函数挖掘的实验，得到准确度和拟合度良好的函数模型，收到了满意的效果．
Gene Expression Programming （GEP） is a new type of self - adaptive evolutionary algorithm which is based on and developd from the advantages of genetic algorithm and genetic programming. It is a new technology. We introduce the current state of the GEP and key technologies, design the strategy of reverse elimination and the improvement program of the no - tree decoding process, aiming to maintain the variety of the population and increase the efficiency of the algorithm. The improved method is applied to the experiment of unitary and polynary function mining in order to obtain a founctional model of high accuracy and fitting degree. A satisfactory result has been achieved.
出处
《山东师范大学学报（自然科学版）》
CAS
2012年第3期28-32,共5页
Journal of Shandong Normal University(Natural Science)
关键词
基因表达式编程
函数挖掘
遗传算法
Gene Expression Programming
function mining
genetic programming
