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.
@article{arxiv.1211.5481,
title = {Genetic Algorithm Modeling with GPU Parallel Computing Technology},
author = {Stefano Cavuoti and Mauro Garofalo and Massimo Brescia and Antonio Pescapé and Giuseppe Longo and Giorgio Ventre},
journal= {arXiv preprint arXiv:1211.5481},
year = {2015}
}
Comments
11 pages, 2 figures, refereed proceedings; Neural Nets and Surroundings, Proceedings of 22nd Italian Workshop on Neural Nets, WIRN 2012; Smart Innovation, Systems and Technologies, Vol. 19, Springer