基于基因表达式编程的递归函数挖掘
Mining Recursive Functions Based on Gene Expression Programming
作者：吴江(四川大学 计算机学院,四川 成都６１００６5)；唐常杰(四川大学 计算机学院,四川 成都６１００６5)；姜玥(四川大学 计算机学院,四川 成都６１００６5)
Author：(School of Computer Sci., Sichuan Univ., Chengdu 610065, China)；(School of Computer Sci., Sichuan Univ., Chengdu 610065, China)；(School of Computer Sci., Sichuan Univ., Chengdu 610065, China)
收稿日期：2006-06-26 年卷（期）页码：2007,39(5):127-132
期刊名称：工程科学与技术
Journal Name：Advanced Engineering Sciences
关键字：基因表达式编程；递归函数；函数挖掘；GEP-RecurMiner
Key words：Gene Expression Programming (GEP); recursive function; function mining; GEP-RecurMiner
基金项目：国家自然科学基金资助项目(60473071);高等学校博士学科点专项科研基金SRFDP(20020610007)
中文摘要
传统基因表达式编程(GEP)无法发现递归函数。为此，分析了传统GEP算法在函数挖掘方面不足的深层次原因，提出了基于递归染色体的基因表达式编程算法GEP RecurMiner和动态进化策略(DSCMS)。理论分析和实验证明了GEP RecurMiner极大地扩充了传统GEP函数挖掘方法的求解空间，能精确地发现传统GEP无法发现的递归函数，同时实验表明动态进化策略有效地提高了GEP RecurMiner函数挖掘算法的效率，挖掘成功率提高20%，平均进化代数下降10%。
英文摘要
Traditional Gene Expression Programming (GEP) is bare of discovering recursive functions. The limitation of function mining of the traditional GEP was analyzed.Revised algorithm GEP RecurMiner based on recursive chromosomes and Dynamic Selection, Crossover and Mutation Strategy (DSCMS) based on best fitness were proposed. The theoretical proof and experiments showed that GEP RecurMiner extremely extends the domain of function mining and can discover recursive functions. The experiments also showed that the performance of GEP RecurMiner is improved by the combination of DSCMS. The number of average evolution generations decreases 10%, and the success rate increases 20%.
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