Mauro Garofalo (2012)
Università degli studi di Napoli Federico II
Abstract
The original work of the present thesis consists of the design and development of a multi-purpose genetic algorithm implemented with the GPGPU/CUDA parallel computing technology. The model comes out from the machine learning supervised paradigm, dealing with both regression and classification scientific problems applied on massive data sets. The model was derived from the original serial implementation, named GAME (Genetic Algorithm Model Experiment) deployed on the DAME Program hybrid distributed infrastructure and made available through the DAMEWARE data mining web application. In such environment the GAME model has been scientifically tested and validated on astrophysics massive data sets problems with successful results (Brescia et al. 2011b).
Relatori:
- Giorgio Ventre – Università degli studi di Napoli Federico II
- Antonio Pescapè – Università degli studi di Napoli Federico II
- Massimo Brescia – INAF OACN