GPSO: A FRAMEWORK FOR OPTIMIZATION OF GENETIC PROGRAMMING CLASSIFIER EXPRESSIONS FOR BINARY CLASSIFICATION USING PARTICLE SWARM OPTIMIZATION

Genetic Programming (GP) is an emerging classification tool known for its flexibility, robustness and lucidity. However, GP suffers from a few limitations like long training time, bloat and lack of convergence. In this paper, we have proposed a hybrid technique that overcomes these drawbacks by improving the performance of GP evolved classifiers using Particle Swarm Optimization (PSO). This hybrid classification technique is a two-step process. In the first phase, we have used GP for evolution of arithmetic classifier expressions (ACE). In the second phase, we add weights to these expressions and optimize them using PSO. We have compared the performance of proposed framework (GPSO) with the GP classification technique over twelve benchmark data sets. The results conclude that the proposed optimization strategy outperforms GP with respect to classification accuracy and less computation.