Solving Classification Problems Using Genetic Programming Algorithms on GPUs
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Cano:2010:HAIS,
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author = "Alberto Cano and Amelia Zafra and Sebastian Ventura",
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title = "Solving Classification Problems Using Genetic
Programming Algorithms on GPUs",
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booktitle = "Hybrid Artificial Intelligence Systems",
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year = "2010",
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series = "Lecture Notes in Computer Science",
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editor = "Emilio Corchado and Manuel {Grana Romay} and
Alexandre {Manhaes Savio}",
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publisher = "Springer",
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pages = "17--26",
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volume = "6077",
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address = "San Sebastian, Spain",
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month = jun # " 23-25",
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DOI = "doi:10.1007/978-3-642-13803-4_3",
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email = "i52caroa@uco.es",
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keywords = "genetic algorithms, genetic programming, gpu, gpgpu,
gpgpgpu",
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size = "10 pages",
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abstract = "Genetic Programming is very efficient in problem
solving compared to other proposals but its performance
is very slow when the size of the data increases. This
paper proposes a model for multi-threaded Genetic
Programming classification evaluation using a NVIDIA
CUDA GPUs programming model to parallelise the
evaluation phase and reduce computational time. Three
different well-known Genetic Programming classification
algorithms are evaluated using the parallel evaluation
model proposed. Experimental results using UCI Machine
Learning data sets compare the performance of the three
classification algorithms in single and multithreaded
Java, C and CUDA GPU code. Results show that our
proposal is much more efficient.",
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affiliation = "University of Cordoba Department of Computing and
Numerical Analysis 14071 Cordoba Spain",
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notes = "JCLEC. No absolute speed measure given (cf.
\cite{langdon:2008:eurogp}). confusion matrix
calculated on two GTX 285. big multi-class training
sets from UCI (poker and shuttle) comparison with Java
and Intel i7 multi-core. Three GP fitness functions.
RPN interpreter",
- }
Genetic Programming entries for
Alberto Cano Rojas
Amelia Zafra Gomez
Sebastian Ventura
Citations