Genetic Algorithm Modeling with GPU Parallel Computing Technology

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...

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Veröffentlicht in:arXiv.org
Hauptverfasser: Cavuoti, Stefano, Garofalo, Mauro, Brescia, Massimo, Pescapé, Antonio, Longo, Giuseppe, Ventre, Giorgio
Format: Paper
Sprache:Englisch
Veröffentlicht: Ithaca Cornell University Library, arXiv.org 23.11.2012
Schlagworte:
Applications programs
Computation
Data mining
Genetic algorithms
Machine learning
Optimization
ISSN:2331-8422
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.1211.5481