Asynchronous Parallel Evolutionary Algorithms: Leveraging Heterogeneous Fitness Evaluation Times for Scalability and Elitist Parsimony Pressure

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@InProceedings{Martin:2015:GECCOcompa,
  author =       "Matthew A. Martin and Alex R. Bertels and 
                 Daniel R. Tauritz",
  title =        "Asynchronous Parallel Evolutionary Algorithms:
                 Leveraging Heterogeneous Fitness Evaluation Times for
                 Scalability and Elitist Parsimony Pressure",
  booktitle =    "GECCO Companion '15: Proceedings of the Companion
                 Publication of the 2015 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming: Poster",
  pages =        "1429--1430",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739482.2764718",
  DOI =          "doi:10.1145/2739482.2764718",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Many important problem classes lead to large
                 variations in fitness evaluation times, such as is
                 often the case in Genetic Programming where the time
                 complexity of executing one individual may differ
                 greatly from that of another. Asynchronous Parallel
                 Evolutionary Algorithms (APEAs) omit the generational
                 synchronization step of traditional EAs which work in
                 well-defined cycles. This paper provides an empirical
                 analysis of the scalability improvements obtained by
                 applying APEAs to such problem classes, aside from the
                 speed-up caused merely by the removal of the
                 synchronization step. APEAs exhibit bias towards
                 individuals with shorter fitness evaluation times,
                 because they propagate faster. This paper demonstrates
                 how this bias can be leveraged in order to provide a
                 unique type of elitist parsimony pressure which rewards
                 more efficient solutions with equal solution quality.",
  notes =        "Also known as \cite{2764718} Distributed at
                 GECCO-2015.",
}

Genetic Programming entries for Matthew A Martin Alex R Bertels Daniel R Tauritz

Citations