Abstract
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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Cavuoti, S., Garofalo, M., Brescia, M., Pescape’, A., Longo, G., Ventre, G. (2013). Genetic Algorithm Modeling with GPU Parallel Computing Technology. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_4
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DOI: https://doi.org/10.1007/978-3-642-35467-0_4
Publisher Name: Springer, Berlin, Heidelberg
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