Abstract
We describe Flex-GP, which we believe to be the first largescale genetic programming cloud computing system. We took advantage of existing software and selected a socket-based, client-server architecture and an island-based distribution model. We developed core components required for deployment on Amazon’s EC2. Scaling the system to hundreds of nodes presented several unexpected challenges and required the development of software for automatically managing deployment, reporting, and error handling. The system’s performance was evaluated on two metrics, performance and speed, on a difficult symbolic regression problem. Our largest successful Flex-GP runs reached 350 nodes and taught us valuable lessons for the next phase of scaling.
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Armbrust, M., Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: Above the clouds: A berkeley view of cloud computing. EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-28 (2009)
Cantú-Paz, E.: A survey of parallel genetic algorithms. Calculateurs Paralleles 10(2) (1998)
Dean, J., Ghemawat, S.: MapReduce: Simplified data processing on large clusters. Communications of ACM 51(1), 107–113 (2008)
Laredo, J.L.J., Castillo, P.A., Paechter, B., Mora, A.M., Alfaro-Cid, E., Esparcia-Alcázar, A.I., Merelo, J.J.: Empirical Validation of a Gossiping Communication Mechanism for Parallel EAs. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 129–136. Springer, Heidelberg (2007)
Luke, S., Panait, L., Balan, G., Paus, S., Skolicki, Z., Bassett, J., Hubley, R., Chircop, A.: ECJ: A Java-based evolutionary computation research system (2007), http://cs.gmu.edu/~eclab/projects/ecj/
Ograph, B., Morgens, Y.: Cloud computing. Communications of the ACM 51(7) (2008)
Poli, R., Langdon, W., McPhee, N.: A field guide to genetic programming. Lulu Enterprises UK Ltd. (2008)
Tomassini, M.: Spatially structured evolutionary algorithms. Springer, Heidelberg (2005)
Vanneschi, L.: Theory and Practice for Efficient Genetic Programming. Ph.D. thesis, Université de Lausanne (2004)
Verma, A., Llora, X., Goldberg, D.E., Campbell, R.H.: Scaling genetic algorithms using MapReduce. In: Proceedings of Intelligent Systems Design and Applications, pp. 13–18 (2009)
Vladislavleva, E., Smits, G., Den Hertog, D.: Order of nonlinearity as a complexity measure for models generated by symbolic regression via Pareto genetic programming. IEEE Transactions on Evolutionary Computation 13(2), 333–349 (2009)
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Sherry, D., Veeramachaneni, K., McDermott, J., O’Reilly, UM. (2012). Flex-GP: Genetic Programming on the Cloud. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_48
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DOI: https://doi.org/10.1007/978-3-642-29178-4_48
Publisher Name: Springer, Berlin, Heidelberg
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