A Comparison of the Generalization Ability of Different Genetic Programming Frameworks



Generalization is an important issue in machine learning. In fact, in several applications good results over training data are not as important as good results over unseen data. While this problem was deeply studied in other machine learning techniques, it has become an important issue for genetic programming only in the last few years. In this paper we compare the generalization ability of several different genetic programming frameworks, including some variants of multi-objective genetic programming and operator equalization, a recently defined bloat free genetic programming system. The test problem used is a hard regression real-life application in the field of drug discovery and development, characterized by a high number of features and where the generalization ability of the proposed solutions is a crucial issue. The results we obtained show that, at least for the considered problem, multi-optimization is effective in improving genetic programming generalization ability, outperforming all the other methods on test data.


Genetic Programming, Overfitting, Multi-Objective


Genetic Programming

Related Project

EnviGP - Improving Genetic Programming for the Environment and Other Applications


IEEE Congress on Evolutionary Computation 2010, July 2010

Cited by

Year 2011 : 2 citations

 Nguyen QU (2011). Examining Semantic Diversity and Semantic Locality of Operators in Genetic Programming. PhD Thesis. School of Computer Science and Informatics, University College Dublin.

 Azad RMA, Ryan C (2011). Variance based selection to improve test set performance in genetic programming. In Genetic and Evolutionary Computation Conference (GECCO 2011), 1315-1322.