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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 activationdoi:10.1007/978-3-540-71701-0_108 dblp:conf/pakdd/WuC07 fatcat:gsiwmc44bvbfjlxrmx2jvuivxu