Strength Through Diversity: Disaggregation and Multi-Objectivisation Approaches for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

@InProceedings{Fieldsend:2015:GECCO,
  author =       "Jonathan E. Fieldsend and Alberto Moraglio",
  title =        "Strength Through Diversity: Disaggregation and
                 Multi-Objectivisation Approaches for Genetic
                 Programming",
  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 =        "1031--1038",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754643",
  DOI =          "doi:10.1145/2739480.2754643",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "An underlying problem in genetic programming (GP) is
                 how to ensure sufficient useful diversity in the
                 population during search. Having a wide range of
                 diverse (sub)component structures available for
                 recombination and/or mutation is important in
                 preventing premature converge. We propose two new
                 fitness disaggregation approaches that make explicit
                 use of the information in the test cases (i.e., program
                 semantics) to preserve diversity in the population. The
                 first method preserves the best programs which pass
                 each individual test case, the second preserves those
                 which are non-dominated across test cases
                 (multi-objectivisation). We use these in standard GP,
                 and compare them to using standard fitness sharing, and
                 using standard (aggregate) fitness in tournament
                 selection. We also examine the effect of including a
                 simple anti-bloat criterion in the selection mechanism.
                 We find that the non-domination approach, employing
                 anti-bloat, significantly speeds up convergence to the
                 optimum on a range of standard Boolean test problems.
                 Furthermore, its best performance occurs with a
                 considerably smaller population size than typically
                 employed in GP.",
  notes =        "Also known as \cite{2754643} 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 Jonathan E Fieldsend Alberto Moraglio

Citations