Using traceless genetic programming for solving multi-objective optimization problems
Mihai Oltean, Crina Groşan · Journal of Experimental & Theoretical Artificial Intelligence · 2007
Traceless genetic programming (TGP) is a genetic programming (GP) variant that is used in cases where the focus is on the output of the program rather 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 will focus on applying TGP to solving multi-objective optimization problems, which are quite unusual in 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 the considered test problems both rapidly and accurately.