Incremental evolution of local search heuristics

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Dara Curran and Eugene Freuder and Thomas Jansen",
  title =        "Incremental evolution of local search heuristics",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "981--982",
  keywords =     "genetic algorithms, genetic programming, incremental
                 evolution, genetic programming, local search
                 heuristics, graph colouring, hyperheuristics, Poster",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830660",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In evolutionary computation, incremental evolution
                 refers to the process of employing an evolutionary
                 environment that becomes increasingly complex over
                 time. We present an implementation of this approach to
                 develop randomised local search heuristics for
                 constraint satisfaction problems, combining research on
                 incremental evolution with local search heuristics
                 evolution. A population of local search heuristics is
                 evolved using a genetic programming framework on a
                 simple problem for a short period and is then allowed
                 to evolve on a more complex problem. Experiments
                 compare the performance of this population with that of
                 a randomly initialised population evolving directly on
                 the more complex problem. The results obtained show
                 that incremental evolution can represent a significant
                 improvement in terms of optimisation speed, solution
                 quality and solution structure.",
  notes =        "Culberon's random graph generator.

                 Also known as \cite{1830660} GECCO-2010 A joint meeting
                 of the nineteenth international conference on genetic
                 algorithms (ICGA-2010) and the fifteenth annual genetic
                 programming conference (GP-2010)",

Genetic Programming entries for Dara Curran Eugene Freuder Thomas Jansen