Memetic Semantic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Robyn Ffrancon and Marc Schoenauer",
  title =        "Memetic Semantic 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 =        "1023--1030",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  note =         "GP Track best paper",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1145/2739480.2754697",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Semantic Backpropagation (SB) was introduced in GP so
                 as to take into account the semantics of a GP tree at
                 all intermediate states of the program execution, i.e.,
                 at each node of the tree. The idea is to compute the
                 optimal should-be values each subtree should return,
                 whilst assuming that the rest of the tree is unchanged,
                 so as to minimize the fitness of the tree. To this end,
                 the Random Desired Output (RDO) mutation operator,
                 proposed in [17], uses SB in choosing, from a given
                 library, a tree whose semantics are preferred to the
                 semantics of a randomly selected subtree from the
                 parent tree. Pushing this idea one step further, this
                 paper introduces the Brando (BRANDO) operator, which
                 selects from the parent tree the overall best subtree
                 for applying RDO, using a small randomly drawn static
                 library. Used within a simple Iterated Local Search
                 framework, BRANDO can find the exact solution of many
                 popular Boolean benchmarks in reasonable time whilst
                 keeping solution trees small, thus paving the road for
                 truly memetic GP algorithms.",
  notes =        "cited by \cite{Alvarez:2016:GECCO}

                 Also known as \cite{2754697} 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 Robyn Ffrancon Marc Schoenauer