Grammar guided genetic programming for flexible neural trees optimization

Authors:
Peng Wu;Yuehui Chen
Affiliations:
School of Information Science and Engineering, Jinan University, Jinan, P.R. China;School of Information Science and Engineering, Jinan University, Jinan, P.R. China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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Abstract

In our previous studies, Genetic Programming (GP), Probabilistic Incremental Program Evolution (PIPE) and Ant Programming (AP) have been used to optimal design of Flexible Neural Tree (FNT). In this paper Grammar Guided Genetic Programming (GGGP) was employed to optimize the architecture of FNT model. Based on the predefined instruction sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The free parameters embedded in the neural tree are optimized by particle swarm optimization algorithm. Empirical results on stock index prediction problems indicate that the proposed method is better than the neural network and genetic programming forecasting models.