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PAKDD
2007
ACM

Grammar Guided Genetic Programming for Flexible Neural Trees Optimization

9 years 8 days ago
Grammar Guided Genetic Programming for Flexible Neural Trees Optimization
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.
Peng Wu, Yuehui Chen
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where PAKDD
Authors Peng Wu, Yuehui Chen
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