FlexGP.py: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InCollection{McDermott:2012:GPTP,
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author = "James McDermott and Kalyan Veeramachaneni and
Una-May O'Reilly",
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booktitle = "Genetic Programming Theory and Practice X",
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year = "2012",
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series = "Genetic and Evolutionary Computation",
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editor = "Rick Riolo and Ekaterina Vladislavleva and
Marylyn D. Ritchie and Jason H. Moore",
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title = "FlexGP.py: Prototyping Flexibly-Scaled,
Flexibly-Factored Genetic Programming for the Cloud",
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publisher = "Springer",
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chapter = "14",
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pages = "205--221",
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address = "Ann Arbor, USA",
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month = "12-14 " # may,
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keywords = "genetic algorithms, genetic programming, C, loud,
Island model, FlexGP, Distributed",
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isbn13 = "978-1-4614-6845-5",
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URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_14",
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DOI = "doi:10.1007/978-1-4614-6846-2_14",
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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'.",
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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
Citations