Asynchronous Parallel Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

@InProceedings{Harter:2017:GECCO,
  author =       "Adam Harter and Daniel R. Tauritz and 
                 William M. Siever",
  title =        "Asynchronous Parallel Cartesian Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "1820--1824",
  size =         "5 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3084210",
  DOI =          "doi:10.1145/3067695.3084210",
  acmid =        "3084210",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, asynchronous parallel evolution,
                 evolutionary computing",
  month =        "15-19 " # jul,
  abstract =     "The run-time of evolutionary algorithms (EAs) is
                 typically dominated by fitness evaluation. This is
                 particularly the case when the genotypes are complex,
                 such as in genetic programming (GP). Evaluating
                 multiple offspring in parallel is appropriate in most
                 types of EAs and can reduce the time incurred by
                 fitness evaluation proportional to the number of
                 parallel processing units. The most naive approach
                 maintains the synchrony of evolution as employed by the
                 vast majority of EAs, requiring an entire generation to
                 be evaluated before progressing to the next generation.
                 Heterogeneity in the evaluation times will degrade the
                 performance, as parallel processing units will idle
                 until the longest evaluation has completed.
                 Asynchronous parallel evolution mitigates this
                 bottleneck and techniques which experience high
                 heterogeneity in evaluation times, such as Cartesian GP
                 (CGP), are prime candidates for asynchrony. However,
                 due to CGP's small population size, asynchrony has a
                 significant impact on selection pressure and biases
                 evolution towards genotypes with shorter execution
                 times, resulting in poorer results compared to their
                 synchronous counterparts. This paper: 1) provides a
                 quick introduction to CGP and asynchronous parallel
                 evolution, 2) introduces asynchronous parallel CGP, and
                 3) shows empirical results demonstrating the potential
                 for asynchronous parallel CGP to outperform synchronous
                 parallel CGP.",
  notes =        "Also known as \cite{Harter:2017:APC:3067695.3084210}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Adam Harter Daniel R Tauritz William M Siever

Citations