Asynchronously evolving solutions with excessively different evaluation time by reference-based evaluation

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@InProceedings{Harada:2014:GECCO,
  author =       "Tomohiro Harada and Keiki Takadama",
  title =        "Asynchronously evolving solutions with excessively
                 different evaluation time by reference-based
                 evaluation",
  booktitle =    "GECCO '14: Proceedings of the 2014 conference on
                 Genetic and evolutionary computation",
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2662-9",
  pages =        "911--918",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2576768.2598330",
  DOI =          "doi:10.1145/2576768.2598330",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The asynchronous evolution has an advantage when
                 evolving solutions with excessively different
                 evaluation time since the asynchronous evolution
                 evolves each solution independently without waiting for
                 other evaluations, unlike the synchronous evolution
                 requires evaluations of all solutions at the same time.
                 As a novel asynchronous evolution approach, this paper
                 proposes Asynchronous Reference-based Evaluation (ARE)
                 that asynchronously selects good parents by the
                 tournament selection using reference solution in order
                 to evolve solutions through a crossover of the good
                 parents. To investigate the effectiveness of ARE in the
                 case of evolving solutions with excessively different
                 evaluation time, this paper applies ARE to Genetic
                 Programming (GP), and compares GP using ARE (ARE-GP)
                 with GP using (mu+lambda) selection ((mu+lambda)-GP) as
                 the synchronous approach in particular situation where
                 the evaluation time of individuals differs from each
                 other. The intensive experiments have revealed the
                 following implications: (1) ARE-GP greatly outperforms
                 (mu+lambda)-GP from the viewpoint of the elapsed unit
                 time in the parallel computation environment, (2)
                 ARE-GP can evolve individuals without decreasing the
                 searching ability in the situation where the computing
                 speed of each individual differs from each other and
                 some individuals fail in their execution.",
  notes =        "Also known as \cite{2598330} GECCO-2014 A joint
                 meeting of the twenty third international conference on
                 genetic algorithms (ICGA-2014) and the nineteenth
                 annual genetic programming conference (GP-2014)",
}

Genetic Programming entries for Tomohiro Harada Keiki Takadama

Citations