Improving the Efficiency Of Genetic Programming for Classification Tasks Using a Phased Approach
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gp-bibliography.bib Revision:1.8098
- @InProceedings{chitty:2024:GECCOcomp2,
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author = "Darren Chitty",
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title = "Improving the Efficiency Of Genetic Programming for
Classification Tasks Using a Phased Approach",
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booktitle = "9th Workshop on Industrial Applications of
Metaheuristics (IAM 2024)",
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year = "2024",
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editor = "Silvino Fernandez Alzueta and Thomas Stuetzle",
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pages = "1702--1705",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, high
performance computing, efficiency",
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isbn13 = "979-8-4007-0495-6",
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DOI = "doi:10.1145/3638530.3664184",
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size = "4 pages",
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abstract = "Genetic Programming (GP) uses Darwinian evolution to
generate algorithms for tasks such as classification
and symbolic regression. However, a drawback is the
interpreter used to evaluate candidate programs adding
significant computational cost. Hence, many studies
have sought to improve the speed of GP primarily via
parallelism. However, efficiency can also offer
considerable performance gains. GP has recently been
applied to combinatorial optimisation using a phased
approach (Phased-GP) whereby programs are evolved
piecemeal avoiding reinterpretation of sub-programs.
This method was found to be highly effective and
efficient compared to standard GP. This paper
investigates if a similar effect is observed when using
phased GP to incrementally build classifiers. Tested
upon known real-world classification problems, an
efficiency saving of up to 98\% can be achieved with a
speedup of 70 fold and no significant loss of
classification accuracy. Moreover, the method can be
easily used within any existing high performance
parallel GP models.",
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notes = "GECCO-2024 IAM A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Darren M Chitty
Citations