It is well known that the numerical solution of evolutionary systems and problems based on topological design requires a high computational power. In the last years, many parallel algorithms have been developed in order to improve its performance. Among them, genetic algorithms (GAs) are one of the most popular metaheuristic algorithms inspired by Darwin's evolution theory. From the High Performance Computing (HPC) point of view, the CUDA environment is probably the parallel computing platform and programming model that more heyday has had in recent years, mainly due to the low acquisition cost of graphics processing units (GPUs) compared to a cluster with similar functional characteristics. Consequently, the number of GPUCUDAs present in the top 500 fastest supercomputers in the world is constantly growing. In this paper, a numerical algorithm developed in the NVIDIA CUDA platform capable of solving classical optimization functions usually employed as benchmarks is presented. The obtained results demonstrate that GPUs are a valuable tool for acceleration of GAs and may enable its use in much complex problems. Also, a sensitivity analysis is carried out in order to show the relative weight of each GA operator in the whole computational cost of the algorithm.
CUDA environment, Genetic algorithm, Mathematical function optimization, GPU architecture, GPU architecture
Primary Language  English 

Subjects  Mathematical Sciences 
Journal Section  Articles 
Authors 

Publication Date  September 30, 2018 
Submission Date  September 12, 2018 
Acceptance Date  September 19, 2018 
Published in Issue  Year 2018 Volume: 1 Issue: 1 
Bibtex  @research article { cams459423, journal = {Communications in Advanced Mathematical Sciences}, issn = {26514001}, address = {}, publisher = {Emrah Evren KARA}, year = {2018}, volume = {1}, number = {1}, pages = {67  83}, doi = {10.33434/cams.459423}, title = {A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture}, key = {cite}, author = {Mroginski, Javier Luis and Castro, Hugo Guillermo} } 
APA  Mroginski, J. L. & Castro, H. G. (2018). A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture . Communications in Advanced Mathematical Sciences , 1 (1) , 6783 . DOI: 10.33434/cams.459423 
MLA  Mroginski, J. L. , Castro, H. G. "A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture" . Communications in Advanced Mathematical Sciences 1 (2018 ): 6783 <https://dergipark.org.tr/en/pub/cams/issue/39351/459423> 
Chicago  Mroginski, J. L. , Castro, H. G. "A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture". Communications in Advanced Mathematical Sciences 1 (2018 ): 6783 
RIS  TY  JOUR T1  A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture AU  Javier LuisMroginski, Hugo GuillermoCastro Y1  2018 PY  2018 N1  doi: 10.33434/cams.459423 DO  10.33434/cams.459423 T2  Communications in Advanced Mathematical Sciences JF  Journal JO  JOR SP  67 EP  83 VL  1 IS  1 SN  26514001 M3  doi: 10.33434/cams.459423 UR  https://doi.org/10.33434/cams.459423 Y2  2018 ER  
EndNote  %0 Communications in Advanced Mathematical Sciences A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture %A Javier Luis Mroginski , Hugo Guillermo Castro %T A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture %D 2018 %J Communications in Advanced Mathematical Sciences %P 26514001 %V 1 %N 1 %R doi: 10.33434/cams.459423 %U 10.33434/cams.459423 
ISNAD  Mroginski, Javier Luis , Castro, Hugo Guillermo . "A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture". Communications in Advanced Mathematical Sciences 1 / 1 (September 2018): 6783 . https://doi.org/10.33434/cams.459423 
AMA  Mroginski J. L. , Castro H. G. A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture. Communications in Advanced Mathematical Sciences. 2018; 1(1): 6783. 
Vancouver  Mroginski J. L. , Castro H. G. A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture. Communications in Advanced Mathematical Sciences. 2018; 1(1): 6783. 
IEEE  J. L. Mroginski and H. G. Castro , "A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture", Communications in Advanced Mathematical Sciences, vol. 1, no. 1, pp. 6783, Sep. 2018, doi:10.33434/cams.459423 