Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7572
Title: Using instruction matrix based genetic programming to evolve programs
Authors: Li, Gang 
Lee, Kin Hong 
Prof. LEUNG Kwong Sak 
Issue Date: 2007
Publisher: Springer Verlag
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, Volume 4683 LNCS, Pages 631 - 640
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 
Abstract: In Genetic Programming (GP), evolving tree nodes separately would be an ideal approach to reduce the huge solution space of GP. We use Instruction Matrix based Genetic Programming (IMGP) to evolve tree nodes separately while taking into account their interdependencies in the form of subtrees. IMGP uses an Instruction Matrix (IM) to maintain the statistical data of tree nodes and subtrees. IMGP extracts program trees from IM, and updates IM with the information of the extracted program trees. The experiments have verified that the results of IMGP are better than those the related GP algorithms in terms of the qualities of the solutions and the number of program evaluations. © Springer-Verlag Berlin Heidelberg 2007.
Type: Conference Paper
URI: http://hdl.handle.net/20.500.11861/7572
ISBN: 978-354074580-8
ISSN: 03029743
DOI: 10.1007/978-3-540-74581-5_69
Appears in Collections:Applied Data Science - Publication

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