- author = "Alberto Cano and Amelia Zafra and Sebastian Ventura",
- title = "Speeding up the evaluation phase of GP classification algorithms on GPUs",
- journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications",
- year = "2012",
- volume = "16",
- number = "2",
- pages = "187--202",
- month = feb,
- keywords = "genetic algorithms, genetic programming, GPU, Computer Science",
- publisher = "Springer Berlin / Heidelberg",
- ISSN = "1432-7643",
-
DOI = "
doi:10.1007/s00500-011-0713-4",
- size = "16 pages",
- abstract = "The efficiency of evolutionary algorithms has become a studied problem since it is one of the major weaknesses in these algorithms. Specifically, when these algorithms are employed for the classification task, the computational time required by them grows excessively as the problem complexity increases. This paper proposes an efficient scalable and massively parallel evaluation model using the NVIDIA CUDA GPU programming model to speed up the fitness calculation phase and greatly reduce the computational time. Experimental results show that our model significantly reduces the computational time compared to the sequential approach, reaching a speedup of up to 820 times. Moreover, the model is able to scale to multiple GPU devices and can be easily extended to any evolutionary algorithm.",
-
notes = "No absolute speed measure given (cf.
\cite{langdon:2008:eurogp}). UCI: Iris, New-thyroid,
Ecoli, Contraceptive, Thyroid, Penbased, Shuttle,
Connect-4, KDDcup, Poker. GTX 285, two GTX 480. 64-bit
Linux Ubuntu.
execution time was reduced from 30 hours to 2 minutes.",
- affiliation = "Department of Computing and Numerical Analysis, University of Cordoba, 14071 Cordoba, Spain",
