Performance Optimization of Multi-Core Grammatical Evolution Generated Parallel Recursive Programs

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Gopinath Chennupati and R. Muhammad Atif Azad and 
                 Conor Ryan",
  title =        "Performance Optimization of Multi-Core Grammatical
                 Evolution Generated Parallel Recursive Programs",
  booktitle =    "GECCO '15: Proceedings 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 =       "978-1-4503-3472-3",
  pages =        "1007--1014",
  keywords =     "genetic algorithms, genetic programming, grammatical
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "",
  DOI =          "doi:10.1145/2739480.2754746",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Although Evolutionary Computation (EC) has been used
                 with considerable success to evolve computer programs,
                 the majority of this work has targeted the production
                 of serial code. Recent work with Grammatical Evolution
                 (GE) produced Multi-core Grammatical Evolution
                 (MCGE-II), a system that natively produces parallel
                 code, including the ability to execute recursive calls
                 in parallel.

                 This paper extends this work by including practical
                 constraints into the grammars and fitness functions,
                 such as increased control over the level of parallelism
                 for each individual. These changes execute the
                 best-of-generation programs faster than the original
                 MCGE-II with an average factor of 8.13 across a
                 selection of hard problems from the literature.

                 We analyze the time complexity of these programs and
                 identify avoiding excessive parallelism as a key for
                 further performance scaling. We amend the grammars to
                 evolve a mix of serial and parallel code, which spawns
                 only as many threads as is efficient given the
                 underlying OS and hardware; this speeds up execution by
                 a factor of 9.97.",
  notes =        "Also known as \cite{2754746} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

Genetic Programming entries for Gopinath Chennupati R Muhammad Atif Azad Conor Ryan