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Using instruction matrix based genetic programming to evolve programs
Author(s)
Date Issued
2007
Publisher
Springer Verlag
ISBN
978-354074580-8
ISSN
03029743
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, Volume 4683 LNCS, Pages 631 - 640
Type
Conference Paper
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.
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