Cloud Driven Design of a Distributed Genetic Programming Platform

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Owen Derby and Kalyan Veeramachaneni and 
                 Una-May O'Reilly",
  title =        "Cloud Driven Design of a Distributed Genetic
                 Programming Platform",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
                 EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
                 EvoRISK, EvoROBOT, EvoSTOC",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and 
                 Ivanoe {De Falco} and Ernesto Tarantino and 
                 Carlos Cotta and Robert Schaefer and Konrad Diwold and 
                 Kyrre Glette and Andrea Tettamanzi and 
                 Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and 
                 Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and 
                 Aniko Ekart and Francisco {Fernandez de Vega} and 
                 Sara Silva and Evert Haasdijk and Gusz Eiben and 
                 Anabela Simoes and Philipp Rohlfshagen",
  series =       "LNCS",
  volume =       "7835",
  publisher =    "Springer Verlag",
  address =      "Vienna",
  publisher_address = "Berlin",
  pages =        "509--518",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, cloud
                 computing, machine learning, distributed evolutionary
                 computation, FlexGP",
  isbn13 =       "978-3-642-37191-2",
  DOI =          "doi:10.1007/978-3-642-37192-9_51",
  size =         "10 pages",
  abstract =     "We describe how we design FlexGP, a distributed
                 genetic programming (GP) system to efficiently run on
                 the cloud. The system has a decentralised,
                 fault-tolerant, cascading startup where nodes start to
                 compute while more nodes are launched. It has a
                 peer-to-peer neighbour discovery protocol which
                 constructs a robust communication network across the
                 nodes. Concurrent with neighbour discovery, each node
                 launches a GP run differing in parametrisation and
                 training data from its neighbors. This factoring of
                 parameters across learners produces many diverse models
                 for use in ensemble learning.",
  notes =        "
                 EvoApplications2013 held in conjunction with
                 EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",

Genetic Programming entries for Owen C Derby Kalyan Veeramachaneni Una-May O'Reilly