  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.
