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",