A Linear Regression Approach to Numerical Simplification in Tree-Based Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @TechReport{Johnston:tr09-7,
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author = "Mark Johnston and Thomas Liddle and Mengjie Zhang",
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title = "A Linear Regression Approach to Numerical
Simplification in Tree-Based Genetic Programming",
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institution = "School of Mathematics Statistics and Operations
Research, Victoria University of Wellington",
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year = "2009",
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type = "Research report",
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number = "09-7",
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address = "New Zealand",
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month = "14 " # dec,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://msor.victoria.ac.nz/twiki/pub/Main/ResearchReportSeries/msor09-07.pdf",
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abstract = "We propose a novel approach to simplification in
tree-based Genetic Programming to combat program bloat,
based upon numerical relaxations of algebraic rules.We
also separate proposal of simplifications (using linear
regression, removing redundant children, and replacing
small ranges with a constant) from an acceptance
criterion that checks the effect of proposed
simplifications on the evaluation of training examples,
looking several levels up the tree.We test our
simplification method on three classification datasets
and conclude that the success of linear regression is
data set dependent, that looking further up the tree
can catch unwanted bad case simplifications, and that
CPU time can be significantly reduced while maintaining
classification accuracy on unseen examples.",
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notes = "Wine, Wisconsin, Coins",
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size = "38 pages",
- }
Genetic Programming entries for
Mark Johnston
Thomas Liddle
Mengjie Zhang
Citations