A Memetic Genetic Programming with decision tree-based local search for classification problems

Pu Wang, Ke Tang, Edward P. K. Tsang, Xin Yao

Abstract: In this work, we propose a new genetic programming algorithm with local search strategies, named Memetic Genetic Programming(MGP), for classification problems. MGP aims to acquire a classifier with large Area Under the ROC Curve (AUC), which has been proved to be a better performance metric for traditionally used metrics (e.g., classification accuracy). Three new points are presented in our new algorithm. First, a new representation called statistical genetic decision tree (SGDT) for GP is proposed on the basis of Genetic Decision Tree (GDT). Second, a new fitness function is designed by using statistic in formation from SGDT. Third, the concept of memetic computing is introduced into SGDT. As a result, the MGP is equipped with a local search method based on the training algorithms for decision trees. The efficacy of the MGP is empirically justified against a number of relevant approaches.