机构地区: 四川大学计算机学院
出 处: 《四川大学学报(工程科学版)》 2007年第1期128-133,共6页
摘 要: 传统GEP(Gene Expression Programm ing)算法存在局部收敛方面的缺陷,为了解决这一问题,提出了可以使进化快速跳出局部最优的VPS-GEP(Various Popu lation Strategy GEP)算法,证明了在概率意义上GEP平均每代进化所耗时间与群体规模成正比,用两个标准测试函数和一个标准测试数据集测试了VPS-GEP算法的函数挖掘能力和效率。实验表明,VPS-GEP算法可以减少进化停滞代数55%以上。 The traditional Gene Expression Programming(GEP) has the deficiency of local optimization. In order to solve this problem, VPS-GEP ( Various Population Strategy GEP), an algorithm for evolution skipping from local optimization fast,was proposed. It was proved that the time for per-generation evolution increases with the size of population under probability sense. The ability of mining function and efficiency of VPS-GEP was tested by two standard test functions and one standard dataset. The experiments showed that VPS-GEP algorithm decreases the generation-stagnancy over 55 %.
领 域: [自动化与计算机技术] [自动化与计算机技术]