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

Authors:

Lingyun Wen,

Guiquan Liu,

Yinghai ZhaoAuthors Info & Claims

GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation

Pages 1005 - 1008

https://doi.org/10.1145/1543834.1543994

Published: 12 June 2009 Publication History

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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.

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Published In

GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation

June 2009

1112 pages

ISBN:9781605583266

DOI:10.1145/1543834

General Chairs:
Lihong Xu
Tongji University, China
,
Erik D. Goodman
Michigan State University, USA
,
Program Chairs:
Guoliang Chen
University of Sci. & Tech. of China
,
Darrell Whitley
Colorado State University, USA
,
Yongsheng Ding
Donghua University, China

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Association for Computing Machinery

New York, NY, United States

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Published: 12 June 2009

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GEC '09: GEC '09 - ACM/SIGEVO Genetic and Evolutionary Computation Summit

June 12 - 14, 2009

Shanghai, China

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On the hardness of full Steiner tree problems

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