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Genetic Algorithm Modeling with GPU Parallel Computing Technology

S. Cavuoti, M. Garofalo, M. Brescia, A. Pescape, G. Longo, G. Ventre
Department of Physics, University Federico II, Via Cinthia 6, I-80126 Napoli, Italy
22nd WIRN, Italian Workshop on Neural Networks, 2012

@article{cavuoti2012genetic,

   title={Genetic Algorithm Modeling with GPU Parallel Computing Technology},

   author={Cavuoti, S. and Garofalo, M. and Brescia, M. and Pescap{‘e}, A. and Longo, G. and Ventre, G.},

   year={2012}

}

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We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.
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