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FlexGP.py: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud

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Genetic Programming Theory and Practice X

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

Running genetic programming on the cloud presents researchers with great opportunities and challenges. We argue that standard island algorithms do not have the properties of elasticity and robustness required to run well on the cloud. We present a prototyped design for a decentralized, heterogeneous, robust, self-scaling, self-factoring, self-aggregating genetic programming algorithm. We investigate its properties using a software “sandbox”.

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Acknowledgements

We would like to thank GE Global Research for the generous funding of this work. Dr. McDermott acknowledges the support of the Irish Research Council for Science, Engineering and Technology co-funded by Marie Curie.

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Authors and Affiliations

  1. Evolutionary Design and Optimization Group, CSAIL, MIT, Cambridge, MA, USA

    James McDermott, Kalyan Veeramachaneni & Una-May O’Reilly

Authors
  1. James McDermott
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  2. Kalyan Veeramachaneni
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  3. Una-May O’Reilly
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Corresponding author

Correspondence to James McDermott .

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Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, West Hall 323, Ann Arbor, 48109, Michigan, USA

    Rick Riolo

  2. Evolved Analytics Europe BVBA, Vossenlaan, Beerse, 2340, Belgium

    Ekaterina Vladislavleva

  3. , Department of Biochemistry and Molecular, The Pennsylvania State University, Wartik Laboratory, University Park, 16802, Pennsylvania, USA

    Marylyn D Ritchie

  4. , Institute for Quantitative, Dartmouth Medical School, Medical Center Drive, Lebanon, 03756, New Hampshire, USA

    Jason H. Moore

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McDermott, J., Veeramachaneni, K., O’Reilly, UM. (2013). FlexGP.py: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud. In: Riolo, R., Vladislavleva, E., Ritchie, M., Moore, J. (eds) Genetic Programming Theory and Practice X. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6846-2_14

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  • DOI: https://doi.org/10.1007/978-1-4614-6846-2_14

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-6845-5

  • Online ISBN: 978-1-4614-6846-2

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