A new two-stage genetic programming classification algorithm and its applications

Abstract

This work aims to find a better solution from an improved genetic programming (GP) algorithm for classification problems without increasing computation costs as far as possible. Firstly, the standard GP algorithm is difficult to succeed in finding better individuals since too large search landscape and it is easy to run into local optimum when evolution reaches a certain stage, while, by enlarging the evolutionary generation or the size of population might increase computation complexity. So, a two-stage GP may be a good solution for these. In the first stage, GP is used to induce a relatively simple classifier and construct features; in the second stage, GP is executed on these features to evolve a better classifier. Secondly, in order to improve the convergence, a new initialization (NI) strategy and a new function operator selection method are designed. In this paper, a NI strategy based two-stage GP algorithm (NITGP) is proposed, and compares with the standard GP on a set of artificial, real-world datasets and image edge detection tasks. The experimental results show that our approach can evolve classifiers with better performance.