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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Wu, P., & Chen, Y. (2007). Grammar guided genetic programming for flexible neural trees optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4426 LNAI, pp. 964–971). Springer Verlag. https://doi.org/10.1007/978-3-540-71701-0_108
Mendeley helps you to discover research relevant for your work.