摘要
基因表达式编程(GEP)是一种基因型和表现型相分离的进化新模型,为了挖掘紧致的函数关系,分析了进化系统各因素对挖掘紧致函数的影响,提出了带紧致压力的适应度函数来进化函数紧致解。实验表明,带有紧致压力的适应度函数能自动进化计算机程序,适合挖掘的紧致关系,在挖掘紧致函数中,朴素基因表达式编程(NGEP)比GEP提高效率21.7%,与不带压力的系统相比,GEP的平均压缩了31.2%,NGEP系统平均压缩了42.5%;NGEP较GEP更容易发现紧致解,且函数表达形式更容易理解,丰富了NGEP理论.
Gene Expression Programming （GEP） is a new member of evolutionary algorithm family, and it is an artificial genotype/phenotype system. Aiming to discover compact mathematical functions for function finding, this study analyzes the factors that greatly affect the efficiency of GEP, proposes the fitness function with pressure parameter, and implements a naive gene expression programming （NGEP） for compact function mining tasks. Extensive experiments show that the proposed fitness function with compact pressure can automatically mine the compact functions as well as an alternative strategy to fred compact results, and NGEP boosts the convergence speed by 21.7% than GEP, in addition, the results are more understandable than that are found by GEP. Compared with the evolution system without compact pressure, the average compact rate are 31.2% in GEP and 42.5% in NGEP, respectively, which shows that NGEP is easier to fred compact results than GEP and the results are more comprehensive than traditional GEP.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2010年第2期284-288,310,共6页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(60773169)
江苏技术师范学院博士启动资金(KYY09001)的资助
关键词
紧致压力
紧致解
函数发现问题
朴素基因表达式编程
compact pressure
compact solving
finding function problem
naive gene expression programming
