Abstract
Genetic Programming is very efficient in problem solving compared to other proposals but its performance is very slow when the size of the data increases. This paper proposes a model for multi-threaded Genetic Programming classification evaluation using a NVIDIA CUDA GPUs programming model to parallelize the evaluation phase and reduce computational time. Three different well-known Genetic Programming classification algorithms are evaluated using the parallel evaluation model proposed. Experimental results using UCI Machine Learning data sets compare the performance of the three classification algorithms in single and multithreaded Java, C and CUDA GPU code. Results show that our proposal is much more efficient.
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
Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, Heidelberg (2002)
Tsakonas, A.: A comparison of classification accuracy of four Genetic Programming-evolved intelligent structures. Information Sciences 176(6), 691–724 (2006)
Bojarczuk, C.C., Lopes, H.S., Freitas, A.A., Michalkiewicz, E.L.: A constrained-syntax Genetic Programming system for discovering classification rules: application to medical data sets. Artificial Intelligence in Medicine 30(1), 27–48 (2004)
Chitty, D.: A data parallel approach to Genetic Programming using programmable graphics hardware. In: GECCO 2007: Proceedings of the Conference on Genetic and Evolutionary Computing, pp. 1566–1573 (2007)
Kirk, D., Hwu, W.-m.W., Stratton, J.: Reductions and Their Implementation. University of Illinois, Urbana-Champaign (2009)
Deb, K.: A population-based algorithm-generator for real-parameter optimization. Soft Computing 9(4), 236–253 (2005)
Genetic Programming on General Purpose Graphics Processing Units, GP GP GPU, http://www.gpgpgpu.com
Harding, S., Banzhaf, W.: Fast Genetic Programming and artificial developmental systems on GPUS. In: HPCS 2007: Proceedings of the Conference on High Performance Computing and Simulation (2007)
De Falco, I., Della Cioppa, A., Tarantino, E.: Discovering interesting classification rules with Genetic Programming. Applied Soft Computing Journal 1(4), 257–269 (2002)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Tan, K.C., Tay, A., Lee, T.H., Heng, C.M.: Mining multiple comprehensible classification rules using Genetic Programming. In: CEC 2002: Proceedings of the Evolutionary Computation on 2002, pp. 1302–1307 (2002)
Langdon, W., Harrison, A.: GP on SPMD parallel graphics hardware for mega bioinformatics data mining. Soft Computing. A Fusion of Foundations, Methodologies and Applications 12(12), 1169–1183 (2008)
NVIDIA Programming and Best Practices Guide 2.3, NVIDIA CUDA Zone, http://www.nvidia.com/object/cuda_home.html
Robilliard, D., Marion-Poty, V., Fonlupt, C.: Genetic programming on graphics processing units. Genetic Programming and Evolvable Machines 10(4), 447–471 (2009)
Ryoo, S., Rodrigues, C.I., Baghsorkhi, S.S., Stone, S.S., Kirk, D.B., Hwu, W.-m.W.: Optimization principles and application performance evaluation of a multithreaded GPU using CUDA. In: PPoPP 2008: Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming, pp. 73–82 (2008)
Ventura, S., Romero, C., Zafra, A., Delgado, J.A., Hervás, C.: JCLEC: A Java framework for evolutionary computation. Soft Computing 12(4), 381–392 (2007)
Back, T., Fogel, D., Michalewicz, Z.: Handbook of Evolutionary Computation. Oxford University Press, Oxford (1997)
Lensberg, T., Eilifsen, A., McKee, T.E.: Bankruptcy theory development and classification via Genetic Programming. European Journal of Operational Research 169(2), 677–697 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cano, A., Zafra, A., Ventura, S. (2010). Solving Classification Problems Using Genetic Programming Algorithms on GPUs. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13803-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-13803-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13802-7
Online ISBN: 978-3-642-13803-4
eBook Packages: Computer ScienceComputer Science (R0)