Solving classification problems using Traceless Genetic Programming
This work addresses classification tasks for machine learning practitioners, but it is incremental as it applies a modified method to existing benchmark data.
The paper tackled classification problems using Traceless Genetic Programming (TGP), a new GP variant that avoids storing evolved programs, and found that TGP performs similarly or better than other GP techniques on PROBEN1 datasets.
Traceless Genetic Programming (TGP) is a new Genetic Programming (GP) that may be used for solving difficult real-world problems. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. In this paper, TGP is used for solving real-world classification problems taken from PROBEN1. Numerical experiments show that TGP performs similar and sometimes even better than other GP techniques for the considered test problems.