基于多表达式基因编程的复杂函数挖掘算法

传统的基因表达式编程(Gene Expression Programming) 挖掘复杂函数时,存在进化辈数过大、无法跳出局部最优解等问题,提出了基于多表达式基因编程的遗传进化算法,提高GEP的全局寻优能力, 提出了一种新的多表达式基因编程的遗传进化算法(Multi Expression Gene Programming, MEGP),建立了同一染色体内基因多层次编码、解码模型,理论上分析并比较了MEGP算法的表达空间复杂性,实现了多表达染色体遗传进化算法和染色体适应度评价算法.实验表明, 在解决函数挖掘问题中, MEGP成功率是传统GEP的2～4倍.

Automatic Complex Function Discovery based on Multi Expression Gene Programming

For complex function mining, traditional gene expression programming (GEP) needs large number of evolutionary generations and may plunge into local optimum. To solve this problem, this paper presents a novel evolutionary algorithm based on multiple expression genes programming (MEGP). The main contributions include: (a) provide a novel gene hierarchical representation model to encode solutions of complex function finding; (b) propose a chromosome architecture that allows of a genome with multiple candidate expressions; (c) theoretically analyze and compare the expression space of MEGP algorithm with traditional GEP; (d) implement the MEGP algorithm and the chromosome fitness evaluation algorithm. Extensive experiments show that the success rate of MEPG is 2-4 times of traditional GEP.