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A Library to Run Evolutionary Algorithms in the Cloud Using MapReduce

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7248))

Abstract

We discuss ongoing development of an evolutionary algorithm library to run on the cloud. We relate how we have used the Hadoop open-source MapReduce distributed data processing framework to implement a single “island” with a potentially very large population. The design generalizes beyond the current, one-off kind of MapReduce implementations. It is in preparation for the library becoming a modeling or optimization service in a service oriented architecture or a development tool for designing new evolutionary algorithms.

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References

  1. Web resource: ApacheHadoop, http://hadoop.apache.org/core

  2. Gunarathne, T., Wu, T.L., Qiu, J., Fox, G.: MapReduce in the clouds for science. In: 2010 IEEE Second International Conference on Cloud Computing Technology and Science, CloudCom (2010)

    Google Scholar 

  3. Web resource: MAHOUT, http://mahout.apache.org/

  4. Jin, C., Vecchiola, C., Buyya, R.: MRPGA: An extension of MapReduce for parallelizing genetic algorithms. In: IEEE Fourth International Conference on eScience 2008, pp. 214–221. IEEE (2008)

    Google Scholar 

  5. Verma, A., Llora, X., Campbell, R., Goldberg, D.: Scaling genetic algorithms using MapReduce. Technical report, Illigal TR 2009007

    Google Scholar 

  6. Verma, A., Llora, X., Venkataraman, S., Goldberg, D., Campbell, R.: Scaling ECGA model building via data-intensive computing. In: 2010 IEEE Congress on Evolutionary Computation, CEC (2010)

    Google Scholar 

  7. Verma, A., Llora, X., Goldberg, D., Campbell, R.: Scaling genetic algorithms using MapReduce. In: Ninth International Conference on Intelligent Systems Design and Applications, ISDA 2009 (2009)

    Google Scholar 

  8. Wang, S., Gao, B.J., Wang, K., Lauw, H.W.: Parallel learning to rank for information retrieval. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information, SIGIR 2011, pp. 1083–1084. ACM, New York (2011)

    Chapter  Google Scholar 

  9. Huang, D.W., Lin, J.: Scaling populations of a genetic algorithm for job shop scheduling problems using MapReduce. In: 2010 IEEE Second International Conference on Cloud Computing Technology and Science, CloudCom (2010)

    Google Scholar 

  10. Verma, A., Zea, N., Cho, B., Gupta, I., Campbell, R.: Breaking the MapReduce stage barrier. In: 2010 IEEE International Conference on Cluster Computing (CLUSTER), pp. 235–244. IEEE (2010)

    Google Scholar 

  11. Web resource: Amazon EC2, http://aws.amazon.com/ec2/

  12. 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)

    Article  Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Fazenda, P., McDermott, J., O’Reilly, UM. (2012). A Library to Run Evolutionary Algorithms in the Cloud Using MapReduce. 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_42

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  • DOI: https://doi.org/10.1007/978-3-642-29178-4_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29177-7

  • Online ISBN: 978-3-642-29178-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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