research-article

A massively parallel Grammatical Evolution technique with OpenCL

Authors:

Igor L.S. Russo,

Heder S. Bernardino,

Helio J.C. BarbosaAuthors Info & Claims

Journal of Parallel and Distributed Computing, Volume 109, Issue C

Pages 333 - 349

https://doi.org/10.1016/j.jpdc.2017.06.017

Published: 01 November 2017 Publication History

Abstract

Grammatical Evolution (GE) is a bio-inspired metaheuristic capable of evolving programs in an arbitrary language using a formal grammar. Among the major applications of the technique, the automatic inference of models from data can be highlighted. As with other genetic programming techniques, GE has a high computational cost. However, the algorithm has steps that can be computed independently, enabling the use of parallel computing to reduce the execution time and, consequently, making it possible its application to larger and more complex problems. Here, models of massively parallel computation for GE are studied and proposed using OpenCL, a framework for the creation of parallel algorithms in heterogeneous computing environments. Computational experiments were conducted to analyze the performance of an implementation using GPUs (Graphics Processing Units), when compared to a sequential implementation in CPUs (Central Processing Units). Finally, speedups of up to 528 were achieved, when all steps are performed in parallel in a GPU. A model of a massively parallel computation for Grammatical Evolution is proposed.Evaluation of the proposed technique using OpenCL.The candidate solutions and data are parallelized.Performance gains of up to 528 were observed.

References

[1]

J. Ando, T. Nagao, Fast evolutionary image processing using Multi-GPUs, in: SMC, IEEE, 2007, pp. 2927-2932.

[2]

R. Arora, R. Tulshyan, K. Deb, Parallelization of binary and real-coded genetic algorithms on GPU using CUDA, in: Congress on Evolutionary Computation, CEC, IEEE, 2010, pp. 1-8.

[3]

D.A. Augusto, H.J.C. Barbosa, Accelerated parallel genetic programming tree evaluation with OpenCL, J. Parallel Distrib. Comput., 73 (2013) 86-100.

Digital Library

[4]

D.A. Augusto, H.S. Bernardino, H.J.C. Barbosa, Predicting the performance of job applicants by means of genetic programming, in: BRICS Congress on Computational Intelligence, 2013, pp. 98-103.

Digital Library

[5]

H.S. Bernardino, H.J.C. Barbosa, Grammar-based immune programming, Nat. Comput., 10 (2011) 209-241.

Digital Library

[6]

A. Cano, J.L. Olmo, S. Ventura, Parallel multi-objective ant programming for classification using {GPUs}, J. Parallel Distrib. Comput., 73 (2013) 713-728.

Digital Library

[7]

A. Cano, S. Ventura, GPU-parallel subtree interpreter for genetic programming, in: Proc. of the Conference on Genetic and Evolutionary Computation (GECCO), ACM, New York, NY, USA, 2014, pp. 887-894.

Digital Library

[8]

A. Cano, A. Zafra, S. Ventura, Speeding up the evaluation phase of GP classification algorithms on GPUs, Soft. Comput., 16 (2012) 187-202.

Digital Library

[9]

A. Cano, A. Zafra, S. Ventura, Parallel evaluation of Pittsburgh rule-based classifiers on GPUs, Neurocomputing, 126 (2014) 45-57.

Digital Library

[10]

A. Cano, A. Zafra, S. Ventura, Speeding up multiple instance learning classification rules on GPUs, Knowledge Inform. Syst., 44 (2015) 127-145.

Digital Library

[11]

D. Chitty, A data parallel approach to genetic programming using programmable graphics hardware, in: Proc. of the Conference on Genetic and Evolutionary Computation (GECCO), vol. 2, ACM Press, 2007, pp. 1566-1573.

Digital Library

[12]

N. Chomsky, Syntactic structures, Mouton de Gruyter, 2002.

[13]

B. Gaster, D. Kaeli, L. Howes, P. Mistry, Heterogeneous Computing with OpenCL, Morgan Kaufmann Pub., 2011.

Digital Library

[14]

F.W. Glover, G.A. Kochenberger, Handbook of Metaheuristics, Springer, 2003.

[15]

A. Gothandaraman, R. Hinde, G. Peterson, Comparing hardware accelerators in scientific applications: A case study, IEEE Trans. Parallel Distrib. Syst., 22 (2011) 58-68.

Digital Library

[16]

S. Harding, W. Banzhaf, Fast genetic programming on GPUs, in: LNCS, vol. 4445, Springer, 2007, pp. 90-101.

Digital Library

[17]

H. Juille, J.B. Pollack, Massively parallel genetic programming, in: Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, 1996, pp. 339-358.

Digital Library

[18]

M. Keijzer, Improving symbolic regression with interval arithmetic and linear scaling, in: European Conference on Genetic Programming, Springer, 2003, pp. 70-82.

Digital Library

[19]

L.B. Kish, End of moores law: thermal (noise) death of integration in micro and nano electronics, Phys. Lett. A, 305 (2002) 144-149.

[20]

D.E. Knuth, Backus normal form vs. Backus Naur form, Commun. ACM, 7 (1964) 735-736.

Digital Library

[21]

M.F. Korns, Accuracy in symbolic regression, in: Genetic Programming Theory and Practice IX, Springer, 2011, pp. 129-151.

[22]

J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press (1992).

Digital Library

[23]

W. Langdon, W. Banzhaf, A SIMD interpreter for genetic programming on GPU graphics cards, in: LNCS, vol. 4971, Springer, 2008, pp. 73-85.

Digital Library

[24]

P. Langley, Lessons for the computational discovery of scientific knowledge, in: Proc. of the Intl. Workshop on Data Mining Lessons Learned, 2002, pp. 9-12.

[25]

L. Ljung, System Identification: Theory for the User, Prentice Hall, Englewood Cliffs, New Jersey, 1987.

Digital Library

[26]

R.I. Mckay, N.X. Hoai, P.A. Whigham, Y. Shan, M. ONeill, Grammar-based genetic programming: a survey, Genetic Progr. Evol. Mach., 11 (2010) 365-396.

Digital Library

[27]

M. Nicolau, A. Agapitos, M. ONeill, A. Brabazon, Guidelines for defining benchmark problems in genetic programming, in: Evolutionary Computation (CEC), 2015 IEEE Congress on, IEEE, 2015, pp. 1152-1159.

[28]

M. ONeill, C. Ryan, Grammatical evolution, IEEE Trans. Evolu. Comput., 5 (2001) 349-358.

Digital Library

[29]

M. ONeill, C. Ryan, Grammatical evolution: Evolutionary automatic programming in a arbitrary language, Kluwer Academic Publishers, 2003.

Digital Library

[30]

J.D. Owens, M. Houston, D. Luebke, S. Green, J.E. Stone, J.C. Phillips, GPU computing, in: Proc. of the IEEE, vol. 96, 5

[31]

M. ONeill, A. Brabazon, Grammatical swarm: The generation of programs by social programming, Nat. Comput., 5 (2006) 443-462.

Digital Library

[32]

P.S. Pacheco, An Introduction to Parallel Programming, Morgan Kaufmann, 2011.

Digital Library

[33]

L. Pagie, P. Hogeweg, Evolutionary consequences of coevolving targets, Evol. Comput., 5 (1997) 401-418.

Digital Library

[34]

S.K. Park, K.W. Miller, Random number generators: Good ones are hard to find, Commun. ACM, 31 (1988) 1192-1201.

Digital Library

[35]

R. Poli, W.B. Langdon, N.F. McPhee, A field guide to genetic programming, 2008. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk

Digital Library

[36]

P. Pospichal, E. Murphy, M. ONeill, J. Schwarz, J. Jaros, Acceleration of grammatical evolution using graphics processing units: Computational intelligence on consumer games and graphics hardware, in: Proc. of the Genetic and Evolutionary Computation Conference, GECCO, ACM, 2011, pp. 431-438.

Digital Library

[37]

D. Robilliard, V. Marion, C. Fonlupt, High performance genetic programming on gpu, in: Proceedings of the 2009 Workshop on Bio-Inspired Algorithms for Distributed Systems, ACM, 2009, pp. 85-94.

Digital Library

[38]

D. Robilliard, V. Marion-Poty, C. Fonlupt, Population parallel GP on the G80 GPU, in: LNCS, Springer, 2008, pp. 98-109.

Digital Library

[39]

I.L.S. Russo, H.S. Bernardino, H.J.C. Barbosa, Evoluo gramatical massivamente paralela com OpenCL via compilao dos modelos candidatos, in: Ibero Latin American Congress on Computational Methods in Engineering, CILAMCE, 2014, pp. 117

[40]

I.L.S. Russo, H.S. Bernardino, H.J.C. Barbosa, A parallel multi-GPU clonal selection algorithm for optimization using OpenCL and OpenMP, in: Computational Intelligence (LA-CCI), 2016 IEEE Latin American Conference on, IEEE, 2016, pp. 1-6.

[41]

D. Tarditi, S. Puri, J. Oglesby, Accelerator: using data parallelism to program gpus for general-purpose uses, in: ACM SIGARCH Computer Architecture News, vol. 34, ACM, 2006, pp. 325-335.

Digital Library

[42]

N.Q. Uy, N.X. Hoai, M. ONeill, R.I. McKay, E. Galvn-Lpez, Semantically-based crossover in genetic programming: application to real-valued symbolic regression, Genet. Programm. Evol. Mach., 12 (2011) 91-119.

Digital Library

[43]

E.J. Vladislavleva, G.F. Smits, D. DenHertog, Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming, IEEE Trans. Evol. Comput., 13 (2009) 333-349.

Digital Library

[44]

D.R. White, J. Mcdermott, M. Castelli, L. Manzoni, B.W. Goldman, G. Kronberger, W. Jakowski, U.-M. OReilly, S. Luke, Better gp benchmarks: community survey results and proposals, Genet. Programm. Evol. Mach., 14 (2013) 3-29.

Digital Library

Cited By

Silva BBernardino HBarbosa H(2021)Human Activity Recognition Using Parallel Cartesian Genetic Programming2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504793(474-481)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1109/CEC45853.2021.9504793
Tang XYang XWu F(2020)Multifractal detrended fluctuation analysis parallel optimization strategy based on openMP for image processingNeural Computing and Applications10.1007/s00521-019-04164-232:10(5599-5608)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1007/s00521-019-04164-2

A massively parallel Grammatical Evolution technique with OpenCL
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  1. Artificial intelligence
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cover image Journal of Parallel and Distributed Computing

Journal of Parallel and Distributed Computing Volume 109, Issue C

November 2017

232 pages

ISSN:0743-7315

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Copyright © Elsevier Inc.

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Academic Press, Inc.

United States

Publication History

Published: 01 November 2017

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Cited By

Silva BBernardino HBarbosa H(2021)Human Activity Recognition Using Parallel Cartesian Genetic Programming2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504793(474-481)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1109/CEC45853.2021.9504793
Tang XYang XWu F(2020)Multifractal detrended fluctuation analysis parallel optimization strategy based on openMP for image processingNeural Computing and Applications10.1007/s00521-019-04164-232:10(5599-5608)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1007/s00521-019-04164-2

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