Abstract
GPU acceleration of increasingly complex variants of evolutionary frameworks typically assume that all the training data used during evolution resides on the GPU. Such an assumption places limits on the style of application to which evolutionary computation can be applied. Conversely, several coevolutionary frameworks explicitly decouple fitness evaluation from the size of the training partition. Thus, a subset of training exemplars is coevolved with the population of evolved individuals. In this work we articulate the design decisions necessary to support Pareto archiving for Genetic Programming under a commodity GPU platform. Benchmarking of corresponding CPU and GPU implementations demonstrates that the GPU platform is still capable of providing a times ten reduction in computation time.
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
Atwater, A., Heywood, M.I., Zincir-Heywood, N.A.: GP under streaming data constraints: A case for Pareto archiving? In: Proceedings of the ACM Genetic and Evolutionary Computation Conference, pp. 703–710 (2012)
Brameier, M., Banzhaf, W.: Linear Genetic Programming. Springer (2007)
Cartlidge, J., Bullock, S.: Combating coevolutionary disengagement by reducing parasite virulence. Evolutionary Computation 12(2), 159–192 (2004)
de Jong, E.D.: A monotonic archive for Pareto-coevolution. Evolutionary Computation 15(1), 61–93 (2007)
Doucette, J.A., McIntyre, A.R., Lichodzijewski, P., Heywood, M.I.: Symbiotic coevolutionary genetic programming: A benchmarking study under large attribute spaces. Genetic Programming and Evolvable Machines 13(1), 71–101 (2012)
Ficici, S.G., Melnik, O., Pollack, J.B.: A game-theoretic and dynamical-systems analysis of selection methods in coevolution. IEEE Transactions on Evolutionary Computation 9(6), 580–602 (2005)
Franco, M.A., Krasnogor, N., Bacardit, J.: Speeding up the evaluation of evolutionary learning systems using GPGPUs. In: Proceedings of the ACM Genetic and Evolutionary Computation Conference, pp. 1039–1046 (2010)
Harding, S., Banzhaf, W.: Implementing Cartesian Genetic Programming Classifiers on graphics processing units using GPU.NET. In: ACM GECCO Computational Intelligence on Consumer Games and Graphics Hardware Workshop, pp. 463–470 (2011)
Hennessy, J.L., Patterson, D.A.: Computer Architecture: A quantitative approach, 2nd edn. Morgan Kaufmann (1996)
Jaros, J., Pospichal, P.: A Fair Comparison of Modern CPUs and GPUs Running the Genetic Algorithm under the Knapsack Benchmark. In: Di Chio, C., Agapitos, A., Cagnoni, S., Cotta, C., de Vega, F.F., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Langdon, W.B., Merelo-Guervós, J.J., Preuss, M., Richter, H., Silva, S., Simões, A., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Togelius, J., Urquhart, N., Uyar, A.Ş., Yannakakis, G.N. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 426–435. Springer, Heidelberg (2012)
Langdon, W.B., Harrison, A.P.: GP on SPMD parallel graphics hardware for mega bioinformatics data mining. Soft Computing 12(12), 1169–1183 (2008)
Lichodzijewski, P., Heywood, M.I.: Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification. In: Proceedings of the ACM Genetic and Evolutionary Computation Conference, pp. 464–471 (2007)
Lichodzijewski, P., Heywood, M.I.: Managing team-based problem solving with Symbiotic Bid-based Genetic Programming. In: Proceedings of the ACM Genetic and Evolutionary Computation Conference, pp. 363–370 (2008)
Pospichal, P., Murphy, E., O’Neill, M., Schwarz, J., Jaros, J.: Acceleration of Grammatical Evolution using graphics processing units. In: ACM GECCO Computational Intelligence on Consumer Games and Graphics Hardware Workshop, pp. 431–438 (2011)
Shao, S., Liu, X., Zhou, M., Zhan, J., Liu, X., Chu, Y., Chen, H.: A gpu-based implementation of an enhanced GEP algorithm. In: Proceedings of the ACM Genetic and Evolutionary Computation Conference, pp. 999–1006 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Turner-Baggs, J.A., Heywood, M.I. (2013). On GPU Based Fitness Evaluation with Decoupled Training Partition Cardinality. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-37192-9_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37191-2
Online ISBN: 978-3-642-37192-9
eBook Packages: Computer ScienceComputer Science (R0)