A Peer-to-Peer Approach to Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{laredo:2011:EuroGP,
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author = "Juan Luis Jim\'enez Laredo and
Daniel {Lombra\~na Gonz\'alez} and Francisco {Fern\'andez de Vega} and
Maribel Garc\'ia Arenas and
Juan Juli\'an {Merelo Guerv\'os}",
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title = "A Peer-to-Peer Approach to Genetic Programming",
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booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
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year = "2011",
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month = "27-29 " # apr,
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editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
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series = "LNCS",
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volume = "6621",
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publisher = "Springer Verlag",
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address = "Turin, Italy",
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pages = "108--117",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-20406-7",
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DOI = "doi:10.1007/978-3-642-20407-4_10",
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abstract = "This paper proposes a fine-grained parallelization of
the Genetic Programming paradigm (GP) using the
Evolvable Agent model evagbp The algorithm is
decentralised in order to take full-advantage of a
massively parallel Peer-to-Peer infrastructure. In this
context, GP is particularly demanding due to its high
requirements of computational power. To assess the
viability of the approach, the evag model has been
empirically analysed in a simulated Peer-to-Peer
environment where experiments were conducted on two
well-known GP problems. Results show that the spatially
structured nature of the algorithm is able to yield a
good quality in the solutions. Additionally,
parallelisation improves times to solution by several
orders of magnitude.",
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notes = "See also \cite{Laredo:thesis}.
Newscast P2P protocol from EU DREAM project. Agents are
lightweight computing threads. Possibly many threads
per node. Emigration within distributed GP population
integrated with stochastic establishment of directly
links in P2P application layer protocol.
Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
- }
Genetic Programming entries for
Juan L J Laredo
Daniel Lombrana Gonzalez Rodriguez
Francisco Fernandez de Vega
Maribel Garcia Arenas
Juan Julian Merelo
Citations