Multi-objective optimization of dynamic memory managers using grammatical evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "J. Manuel Colmenar and Jose L. Risco-Martin and 
                 David Atienza and J. Ignacio Hidalgo",
  title =        "Multi-objective optimization of dynamic memory
                 managers using grammatical evolution",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1819--1826",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, SBSE, Real world applications",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001820",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  size =         "8 pages",
  abstract =     "The dynamic memory manager (DMM) is a key element
                 whose customization for a target application reports
                 great benefits in terms of execution time, memory usage
                 and energy consumption. Previous works presented
                 algorithms to automatically obtain custom DMMs for a
                 given application. Nevertheless, those approaches are
                 based on grammatical evolution where the fitness is
                 built as an aggregate objective function, which does
                 not completely exploit the search space, returning the
                 designer the DMM solution with best fitness. However,
                 this approach may not find solutions that could fit in
                 a concrete hardware platform due to a very low value of
                 one of the objectives while the others remain high,
                 which may represent a high fitness. In this work we
                 present the first multi-objective optimisation
                 methodology applied to DMM optimisation where the
                 Pareto dominance is considered, thus providing the
                 designer with a set of non-dominated DMM
                 implementations on each optimisation run. Our results
                 show that the multi-objective optimisation provides
                 Pareto-optimal alternatives due to a better
                 exploitation of the search space obtaining better
                 hypervolume values than the aggregate objective
                 function approach.",
  notes =        "Garbage collector. Energy consumption. NSGA-2.

                 Also known as \cite{2001820} GECCO-2011 A joint meeting
                 of the twentieth international conference on genetic
                 algorithms (ICGA-2011) and the sixteenth annual genetic
                 programming conference (GP-2011)",

Genetic Programming entries for J Manuel Colmenar Jose L Risco-Martin David Atienza Alonso Jose Ignacio Hidalgo Perez