Grammar Guided Genetic Programming for Flexible Neural Trees Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/pakdd/WuC07,

• author = "Peng Wu and Yuehui Chen",
• title = "Grammar Guided Genetic Programming for Flexible Neural Trees Optimization",
• booktitle = "Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007",
• year = "2007",
• editor = "Zhi-Hua Zhou and Hang Li and Qiang Yang",
• volume = "4426",
• series = "Lecture Notes in Computer Science",
• pages = "964--971",
• address = "Nanjing, China",
• month = may # " 22-25",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-71700-3",
• DOI = "doi:10.1007/978-3-540-71701-0_108",
• size = "8 pages",
• 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 pre-defined 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.",
• bibdate = "2007-06-24",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/pakdd/pakdd2007.html#WuC07",

}

Genetic Programming entries for Peng Wu Yuehui Chen

Citations