CAJ | 학술논문

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.
기인표체식편정(GEP)시일충기인형화표현형상분리적진화신모형,위료알굴긴치적함수관계,분석료진화계통각인소대알굴긴치함수적영향,제출료대긴치압력적괄응도함수래진화함수긴치해.실험표명,대유긴치압력적괄응도함수능자동진화계산궤정서,괄합알굴적긴치관계,재알굴긴치함수중,박소기인표체식편정(NGEP)비GEP제고효솔21.7%,여불대압력적계통상비,GEP적평균압축료31.2%,NGEP계통평균압축료42.5%;NGEP교GEP경용역발현긴치해,차함수표체형식경용역리해,봉부료NGEP이론.
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 na(l)ve 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 find 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 find compact results than GEP and the results are more comprehensive than traditional GEP.