Abstract
The work presented in this paper consists in an adaptation of a Genetic Algorithm (GA) to perform variable selection in an heterogeneous cluster where the nodes are themselves clusters of GPUs. Due to this heterogeneity, several mechanisms to perform a load balance will be discussed as well as the optimization of the fitness function to take advantage of the GPUs available. The algorithm will be compared with previous parallel implementations analysing the advantages and disadvantages of the approach, showing that for large data sets, the proposed approach is the only one that can provide a solution.
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
Alba, E., Nebro, A.J., Troya, J.M.: Heterogeneous computing and parallel genetic algorithms. Journal of Parallel and Distributed Computing 62, 1362–1385 (2002)
Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. on Evolutionary Computation 6(5), 443–462 (2002)
Bellman, R.E.: Adaptive control processes - A guided tour. Princeton University Press, Princeton (1961)
CantĂş-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Massachusetts (2000)
Eirola, E., Liitiäinen, E., Lendasse, A., Corona, F., Verleysen, M.: Using the delta test for variable selection. In: ESANN 2008: European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 25–30 (April 2008)
Garcia, V., Debreuve, E., Barlaud, M.: Fast k nearest neighbor search using GPU. In: CVPR Workshop on Computer Vision on GPU (2008)
Guillén, A., González, J., Rojas, I., Pomares, H., Herrera, L.J., Valenzuela, O., Rojas, F.: Output Value-Based Initialization For Radial Basis Function Neural Networks. Neural Processing Letters (June 2007), doi:10.1007/s11063-007-9039-8
Guillén, A., Rojas, I., González, J., Pomares, H., Herrera, L.J., Paechter, B.: Improving the Performance of Multi-objective Genetic Algorithm for Function Approximation Through Parallel Islands Specialisation. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1127–1132. Springer, Heidelberg (2006)
Guillén, A., Rojas, I., González, J., Pomares, H., Herrera, L.J., Paechter, B.: Boosting the performance of a multiobjective algorithm to design rBFNNs through parallelization. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4431, pp. 85–92. Springer, Heidelberg (2007)
Guillén, A., Pomares, H., González, J., Rojas, I., Valenzuela, O., Prieto, B.: Parallel multiobjective memetic rbfnns design and feature selection for function approximation problems. Neurocomputing 72(16-18), 3541–3555 (2009)
Guillen, A., Sovilj, D., Lendasse, A., Mateo, F., Rojas, I.: Minimising the delta test for variable selection in regression problems. Int. J. High Perform. Syst. Archit. 1, 269–281 (2008)
Herrera, F., Lozano, M.: Gradual distributed real-coded genetic algorithms. IEEE Transactions on Evolutionary Computation 4(1), 43 (2000)
Herrera, L.J., Pomares, H., Rojas, I., Verleysen, M., Guilén, A.: Effective input variable selection for function approximation. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 41–50. Springer, Heidelberg (2006)
Hiroyasu, T., Miki, M., Negami, M.: Distributed genetic algorithms with randomized migration rate. In: Proceedings of the IEEE Conf. Systems, Man and Cybernetics, pp. 689–694 (1999)
Pi, H., Peterson, C.: Finding the embedding dimension and variable dependencies in time series. Neural Computation 6(3), 509–520 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guillén, A. et al. (2011). Variable Selection in a GPU Cluster Using Delta Test. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21501-8_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-21501-8_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21500-1
Online ISBN: 978-3-642-21501-8
eBook Packages: Computer ScienceComputer Science (R0)