开放尾部的基因表达式程序设计

基因表达式程序设计（GEP）的染色体由具有特殊限制的头、尾组成，并要求尾部符号严格取自基本的终端集。这一做法作用明了、易于表述，基本为现有GEP所采纳，但不利于语义计算的重用。谋求突破尾部限制条件，探究一种开放尾部的新型GEP算法。该算法将运行过程产生的优良个体动态地引入种群个体的基因，从而实现运算精度的提升。符号回归实验表明，开放尾部的GEP算法在平均精度性能上要优于主流GEP方法。

Open tail gene expression programming

Gene Expression Programming（GEP） genes are structurally organized in a head and a tail with special restrictions and require every symbol in tail must be strictly taken from terminal set. This practice is basically adopted by existing GEP for its perspicuous effect and facility to express, but it is not conducive to semantic computing reuse. This paper seeks to break the restriction on tail and searches a novel open tail GEP algorithm. This algorithm can improve the precision of computing by dynamically introducing the excellent individuals generated during program running to the genes of individuals in a group. The results of symbolic regression experiments show that open tail GEP algorithm outperforms mainstream GEP on average precision performance.