Deep Parameter Optimisation

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

  author =       "Fan Wu and Westley Weimer and Mark Harman and 
                 Yue Jia and Jens Krinke",
  title =        "Deep Parameter Optimisation",
  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",
  pages =        "1375--1382",
  organisation = "SIGEVO",
  address =      "Madrid",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "11-15 " # jul,
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, SBSE, MOGA, Parameter tuning, parameter
                 exposure, memory allocation",
  isbn13 =       "978-1-4503-3472-3",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1145/2739480.2754648",
  abstract =     "We introduce a mutation-based approach to
                 automatically discover and expose deep (previously
                 unavailable) parameters that affect a program's runtime
                 costs. These discovered parameters, together with
                 existing (shallow) parameters, form a search space that
                 we tune using search-based optimisation in a
                 bi-objective formulation that optimises both time and
                 memory consumption. We implemented our approach and
                 evaluated it on four real-world programs. The results
                 show that we can improve execution time by 12percent or
                 achieve a 21percent memory consumption reduction in the
                 best cases. In three subjects, our deep parameter
                 tuning results in a significant improvement over the
                 baseline of shallow parameter tuning, demonstrating the
                 potential value of our deep parameter extraction
  notes =        "Entered 2016 HUMIES

                 AST, NSGA-II, malloc, dlmalloc, Milu. 70000 lines of C
                 code: Expresso, gawk, flex, sed.

                 GECCO-2015 A joint meeting of the twenty fourth
                 international conference on genetic algorithms
                 (ICGA-2015) and the twentieth annual genetic
                 programming conference (GP-2015)",

Genetic Programming entries for Fan Wu Westley Weimer Mark Harman Yue Jia Jens Krinke