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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{McDermott:2012:GPTP,

• 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

Citations