Algorithm configuration using GPU-based metaheuristics - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2013

Algorithm configuration using GPU-based metaheuristics


In this paper, a GPU-based implementation of Differential Evolution (DE) and Particle Swarm Optimization (PSO) in CUDA is used to automatically tune the parameters of PSO. The parameters were tuned over a set of 8 problems and then tested over 20 problems to assess the generalization ability of the tuners. We compare the results obtained using such parameters with the 'standard' ones proposed in the literature and the ones obtained by state-of-the-art tuning methods (irace). The results are comparable to the ones suggested for the standard version of PSO (SPSO), and the ones obtained by irace, while the GPU implementation makes tuning time acceptable. To the best of our knowledge, this is the first time that a general purpose library of GPU-based metaheuristics is used to solve this problem, as well as being one of the few cases where DE and PSO are both used as tuners.
Fichier principal
Vignette du fichier
gecco2pages_noCopyright.pdf (175.03 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01221570 , version 1 (28-10-2015)



Roberto Ugolotti, Youssef S.G. Nashed, Pablo Mesejo, Stefano Cagnoni. Algorithm configuration using GPU-based metaheuristics. 15th Genetic and Evolutionary Computation Conference companion (GECCO’13), Jul 2013, Amsterdam, Netherlands. pp.221-222, ⟨10.1145/2464576.2464682⟩. ⟨hal-01221570⟩
39 View
173 Download



Gmail Facebook Twitter LinkedIn More