Traceless Genetic Programming (TGP) is a Genetic Programming (GP) variant that is used in cases where the focus is rather the output of the program than the program itself. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. Two genetic operators are used in conjunction with TGP: crossover and insertion. In this paper, we shall focus on how to apply TGP for solving multi-objective optimization problems which are quite unusual for GP. Each TGP individual stores the output of a computer program (tree) representing a point in the search space. Numerical experiments show that TGP is able to solve very fast and very well the considered test problems.
翻译:无痕遗传方案(TGP)是一种遗传方案(GP)变体,在焦点是程序产出而不是程序本身的情况下使用。TGP和其他GP技术的主要区别是,TGP没有明确地储存进化的计算机程序。两个基因操作员与TGP一起使用:交叉和插入。在本文件中,我们将侧重于如何应用TGP解决对GP来说非常不寻常的多目标优化问题。每个TGP个人存储代表搜索空间一个点的计算机程序(Tree)的输出。数字实验显示,TGP能够非常快速和很好地解决考虑过的测试问题。