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2005
IEEE

On improving genetic programming for symbolic regression

9 years 8 months ago
On improving genetic programming for symbolic regression
This paper reports an improvement to genetic programming (GP) search for the symbolic regression domain, based on an analysis of dissimilarity and mating. GP search is generally difficult to characterise for this domain, preventing well motivated algorithmic improvements. We first examine the ability of various solutions to contribute to the search process. Further analysis highlights the numerous solutions produced during search with no change to solution quality. A simple algorithmic enhancement is made that reduces these events and produces a statistically significant improvement in solution quality. We conclude by verifying the generalisability of these results on several other regression instances.
Steven Gustafson, Edmund K. Burke, Natalio Krasnog
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where CEC
Authors Steven Gustafson, Edmund K. Burke, Natalio Krasnogor
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