Abstract
The Flash system runs ensemble-based Genetic Programming (GP) symbolic regression on a shared memory desktop. To significantly reduce the high time cost of the extensive model predictions required by symbolic regression, its fitness evaluations are tasked to the desktop’s GPU. Successive GP “instances” are run on different data subsets and randomly chosen objective functions. Best models are collected after a fixed number of generations and then fused with an adaptive, output-space method. New instance launches are halted once learning is complete. We demonstrate that Flash’s ensemble strategy not only makes GP more robust, but it also provides an informed online means of halting the learning process. Flash enables GP to learn from a dataset composed of 370K exemplars and 90 features, evolving a population of 1000 individuals over 100 generations in as few as 50 seconds.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Banzhaf, W., Harding, S., Langdon, W., Wilson, G.: Accelerating genetic programming through graphics processing units. In: Genetic Programming Theory and Practice VI. Genetic and Evolutionary Computation, pp. 1–19. Springer US (2009)
Bertin-Mahieux, T., Ellis, D.P., Whitman, B., Lamere, P.: The million song dataset. In: Proceedings of the 12th International Conference on Music Information Retrieval, ISMIR 2011 (2011)
Chitty, D.M.: A data parallel approach to genetic programming using programmable graphics hardware. In: Proceedings of the 9th Annual GECCO Conference, GECCO 2007, pp. 1566–1573. ACM, New York (2007)
Dijkstra, E.W.: Algol 60 translation. Supplement, Algol 60 Bulletin 10 (1960)
Harding, S., Banzhaf, W.: Fast genetic programming on GPUs. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 90–101. Springer, Heidelberg (2007)
Harding, S., Banzhaf, W.: Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET. In: Proceedings of the 13th GECCO Conference, GECCO 2011, pp. 463–470. ACM, New York (2011)
Harding, S.L., Banzhaf, W.: Distributed genetic programming on GPUs using CUDA. In: Hidalgo, I., Fernandez, F., Lanchares, J. (eds.) PABA Workshop, Raleigh, NC, USA, September 13, pp. 1–10 (2009)
Kotanchek, M., Smits, G., Vladislavleva, E.: Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models. In: Riolo, R., Soule, T., Worzel, B. (eds.) Genetic Programming Theory and Practice V. Genetic and Evolutionary Computation Series, pp. 201–220. Springer US (2008)
Langdon, W.B., Banzhaf, W.: A SIMD interpreter for genetic programming on GPU graphics cards. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 73–85. Springer, Heidelberg (2008)
Langdon, W.: A CUDA SIMT interpreter for genetic programming. Tech. Rep. TR-09-05, Department of Computer Science, Strand (June 2009) (revised)
Langdon, W.B.: A many threaded CUDA interpreter for genetic programming. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 146–158. Springer, Heidelberg (2010)
Lewis, T.E., Magoulas, G.D.: Strategies to minimise the total run time of cyclic graph based genetic programming with GPUs. In: Proceedings of the 11th GECCO Conference, GECCO 2009, pp. 1379–1386. ACM, New York (2009)
Maitre, O., Querry, S., Lachiche, N., Collet, P.: EASEA parallelization of tree-based Genetic Programming. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)
Maitre, O., Lachiche, N., Collet, P.: Fast evaluation of GP trees on GPGPU by optimizing hardware scheduling. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 301–312. Springer, Heidelberg (2010)
NVIDIA Corporation: NVIDIA CUDA C programming guide, version 3.2 (2010)
Robilliard, D., Marion-Poty, V., Fonlupt, C.: Population parallel GP on the G80 GPU. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 98–109. Springer, Heidelberg (2008)
Robilliard, D., Marion-Poty, V., Fonlupt, C.: Genetic programming on graphics processing units. Genetic Programming and Evolvable Machines 10(4), 447–471 (2009)
Veeramachaneni, K., Derby, O., Sherry, D., O’Reilly, U.M.: Learning regression ensembles with genetic programming at scale. In: Proceeding of the Fifteenth GECCO Conference, GECCO 2013, pp. 1117–1124. ACM, New York (2013)
Wilson, G., Banzhaf, W.: Linear genetic programming GPGPU on Microsoft Xbox 360. In: IEEE Congress on Evolutionary Computation, pp. 378–385 (2008)
Yang, Y.: Adaptive regression by mixing. Journal of the American Statistical Association 96(454), 574–588 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arnaldo, I., Veeramachaneni, K., O’Reilly, UM. (2014). Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets. In: Nicolau, M., et al. Genetic Programming. EuroGP 2014. Lecture Notes in Computer Science, vol 8599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44303-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-662-44303-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44302-6
Online ISBN: 978-3-662-44303-3
eBook Packages: Computer ScienceComputer Science (R0)