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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "James McDermott and Kalyan Veeramachaneni and 
                 Una-May O'Reilly",
  booktitle =    "Genetic Programming Theory and Practice X",
  year =         "2012",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Marylyn D. Ritchie and Jason H. Moore",
  title =        "FlexGP.py: Prototyping Flexibly-Scaled,
                 Flexibly-Factored Genetic Programming for the Cloud",
  publisher =    "Springer",
  chapter =      "14",
  pages =        "205--221",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  keywords =     "genetic algorithms, genetic programming, C, loud,
                 Island model, FlexGP, Distributed",
  isbn13 =       "978-1-4614-6845-5",
  URL =          "http://dx.doi.org/10.1007/978-1-4614-6846-2_14",
  DOI =          "doi:10.1007/978-1-4614-6846-2_14",
  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
                 decentralised, heterogeneous, robust, self-scaling,
                 self-factoring, self-aggregating genetic programming
                 algorithm. We investigate its properties using a
                 software 'sandbox'.",
  notes =        "part of \cite{Riolo:2012:GPTP} published after the
                 workshop in 2013",

Genetic Programming entries for James McDermott Kalyan Veeramachaneni Una-May O'Reilly