A Library to Run Evolutionary Algorithms in the Cloud using MapReduce

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{fazenda:evoapps12,
  author =       "Pedro Fazenda and James McDermott and 
                 Una-May O'Reilly",
  title =        "A Library to Run Evolutionary Algorithms in the Cloud
                 using {MapReduce}",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications2012: {EvoCOMNET}, {EvoCOMPLEX},
                 {EvoFIN}, {EvoGAMES}, {EvoHOT}, {EvoIASP}, {EvoNUM},
                 {EvoPAR}, {EvoRISK}, {EvoSTIM}, {EvoSTOC}",
  year =         "2012",
  month =        "11-13 " # apr,
  editor =       "Cecilia {Di Chio} and Alexandros Agapitos and 
                 Stefano Cagnoni and Carlos Cotta and F. {Fernandez de Vega} and 
                 Gianni A. {Di Caro} and Rolf Drechsler and 
                 Aniko Ekart and Anna I Esparcia-Alcazar and Muddassar Farooq and 
                 William B. Langdon and Juan J. Merelo and 
                 Mike Preuss and Hendrik Richter and Sara Silva and 
                 Anabela Simoes and Giovanni Squillero and Ernesto Tarantino and 
                 Andrea G. B. Tettamanzi and Julian Togelius and 
                 Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis",
  series =       "LNCS",
  volume =       "7248",
  publisher =    "Springer Verlag",
  address =      "Malaga, Spain",
  pages =        "416--425",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, MapReduce,
                 Hadoop, EC, Amazon EC2, FlexEA",
  isbn13 =       "978-3-642-29177-7",
  DOI =          "doi:10.1007/978-3-642-29178-4_42",
  size =         "10 pages",
  abstract =     "We discuss ongoing development of an evolutionary
                 algorithm library to run on the cloud. We relate how we
                 have used the Hadoop open-source MapReduce distributed
                 data processing framework to implement a single
                 `island' with a potentially very large population. The
                 design generalises beyond the current, one-off kind of
                 MapReduce implementations. It is in preparation for the
                 library becoming a modelling or optimization service in
                 a service oriented architecture or a development tool
                 for designing new evolutionary algorithms.",
  notes =        "EDO-Lib, Reporter, Island Model, HDFS, p419
                 'FitnessEvaluator can be set by injection'. Matlab.
                 Does not give execution time in terms of GP operation
                 per second. Population up to 1 million. XEN.org p423
                 'takes much longer to design a MapReduce implementation
                 than it would to develop a socket or MPI model.' p424
                 'It also results in a code base which requires more
                 effort to support and maintain which impacts research
                 agility.'

                 Part of \cite{DiChio:2012:EvoApps} EvoApplications2012
                 held in conjunction with EuroGP2012, EvoCOP2012,
                 EvoBio'2012 and EvoMusArt2012",
}

Genetic Programming entries for Pedro Vicoso Fazenda James McDermott Una-May O'Reilly

Citations