HS-Model: a hierarchical statistical subtree-generating model for genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{WenLZ:2009:GEC,

• author = "Lingyun Wen and Guiquan Liu and Yinghai Zhao",
• title = "HS-Model: a hierarchical statistical subtree-generating model for genetic programming",
• booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation",
• year = "2009",
• editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• pages = "1005--1008",
• address = "Shanghai, China",
• organisation = "SigEvo",
• DOI = "doi:10.1145/1543834.1543994",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = jun # " 12-14",
• isbn13 = "978-1-60558-326-6",
• keywords = "genetic algorithms, genetic programming, Poster",
• abstract = "In genetic programming with subtrees, two issues are crucial: how to acquire promising subtrees efficiently and how to keep these subtrees to be used repeatedly in the evolutional process. In this paper, we propose a hierarchical statistical model for program trees, named HS-Model, to deal with both the above issues. The HS-Model conducts statistic analysis of the current population and generates superior subtrees automatically with efficiency. The HS-Model leaves out the tedious operations to keep the promising subtrees for reusing and also omits updating the subtree library according to certain criterion. Experimental results on solving the classical artificial ant problem proved the effectiveness and the efficiency of our proposed method.",
• notes = "Also known as \cite{DBLP:conf/gecco/WenLZ09} part of \cite{DBLP:conf/gec/2009}",

}

Genetic Programming entries for Lingyun Wen Guiquan Liu Yinghai Zhao

Citations