Abstract
We describe the parallel implementation of an evolutionary programming algorithm for minimization of nonlinear, continuous, realvalued functions of n variables. The parallel implementation was carried using the GPGPU (General-Purpose Computing on Graphics Processing Units) technique. Evolutionary programming (EP) was selected from the available evolutionary algorithm paradigms because it presents low dependency between its genetic operators. This feature provided a particular advantage to parallelize the mutation and evaluation stages in EP using a master-slave model. The obtained results report a linear speed up with respect to the number of cores in the test platform.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Wiley, New York (1966)
Fogel, D.B.: Evolutionary Computation: Towards a New Philosophy of Machine Intelligence. IEEE Press, New York (1995)
Fogel, D.B., Stayton, L.C.: On the effectiveness of crossover in simulated evolutionary optimization. Bio. Syst. 32(3), 171–182 (1994)
Tongchim, S., Yao, X.: Parallel evolutionary programming. In: Proceedings of the IEEE Congress on Evolutionary Computing, Portland, OR, pp. 1362–1367 (2004)
Fernández, F., Tomassini, M., Vanneschi, L.: Studying the influence of communication topology and migration on distributed genetic programming. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, p. 51. Springer, Heidelberg (2001)
Fernández, F., Tomassini, M., Punch, W.F., Sánchez, J.M.: Experimental study of multipopulation parallel genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 283–293. Springer, Heidelberg (2000)
Tanese, R.: Distributed genetic algorithms. In: Schaffer, J.D., (ed.) ICGA-3, pp. 434–439(1989)
Alba, E., Troya, J.M.: Influence of the migration policy in parallel distributed gas with structured and panmictic populations. Appl. Intell. 12(3), 163–181 (2000)
Cantu-Paz, E., Goldberg, D.E.: Predicting speedups of idealized bounding cases of parallel genetic algorithms. In: Bäck, T., (ed.) Proceedings of the 7th International Conference on Genetic Algorithms, pp. 113–120 (1997)
Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. Evol. Comput. 6(5), 443–462 (2002)
Levine, D.: Users guide to the PGAPack parallel genetic algorithm library. Argonne Nat. Lab., Math. Comput. Sci. Div., Tech. Rep. ANL-95/18, January 1995
Gebali, F.: Algorithms and Parallel Computing. Wiley, Hoboken (2011)
Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison Wesley, Upper Saddle River (2011)
Kirk, D.B., Huw, W.M.W.: Programming Massively Parallel Processors: A Hands-On Approach. Morgan Kaufmann, San Francisco (2010)
Aziz, N.I.A., Sulaiman, S.I., Musikin, I., Shaari, S.: Assessment of evolutionary programming models for single-objective optimization. In: Musirin, I., Salimin, R.H. (eds.) Proceedings of the 7th IEEE International PEOCO, Langkawi, Malaysia, pp. 304–308 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Almeida Arrieta, B.J., Alvarado-Nava, O., Chablé Martínez, H.M., Rodríguez-Martínez, E., Zaragoza Martínez, F.J. (2016). Parallel Implementation of an Evolutionary Algorithm for Function Minimization on a GPGPU. In: Gitler, I., Klapp, J. (eds) High Performance Computer Applications. ISUM 2015. Communications in Computer and Information Science, vol 595. Springer, Cham. https://doi.org/10.1007/978-3-319-32243-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-32243-8_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32242-1
Online ISBN: 978-3-319-32243-8
eBook Packages: Computer ScienceComputer Science (R0)