Injecting Social Diversity in Multi-Objective Genetic Programming: the Case of Model Well-formedness Rule Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{2018_Batot_Sahraoui_SSBSE18,
  author =       "Edouard Batot and Houari Sahraoui",
  title =        "Injecting Social Diversity in Multi-Objective Genetic
                 Programming: the Case of Model Well-formedness Rule
                 Learning",
  booktitle =    "SSBSE 2018",
  year =         "2018",
  editor =       "Thelma Elita Colanzi and Phil McMinn",
  volume =       "11036",
  series =       "LNCS",
  pages =        "166--181",
  address =      "Montpellier, France",
  month =        "8-9 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, SBSE, Model
                 Driven Engineering (MDE), Metamodel, Modelling Space,
                 WFR, OCL, NSGA-II, SSDM, TF-IDF",
  isbn13 =       "978-3-319-99241-9",
  URL =          "http://www-ens.iro.umontreal.ca/~batotedo/papers/2018_Batot_Sahraoui_SSBSE18.pdf",
  DOI =          "doi:10.1007/978-3-319-99241-9_8",
  size =         "15 pages",
  abstract =     "Software modelling activities typically involve a
                 tedious and time-consuming effort by specially trained
                 personnel. This lack of automation hampers the adoption
                 of the Model Driven Engineering (MDE) paradigm.
                 Nevertheless, in the recent years, much research work
                 has been dedicated to learn MDE artefacts instead of
                 writing them manually. In this context, mono- and
                 multi-objective Genetic Programming (GP) has proven
                 being an efficient and reliable method to derive
                 automation knowledge by using, as training data, a set
                 of input/out examples representing the expected
                 behaviour of an artefact. Generally, the conformance to
                 the training example set is the main objective to lead
                 the search for a solution. Yet, single fitness peak, or
                 local optima deadlock, one of the major drawbacks of
                 GP, happens when adapted to MDE and hinder the results
                 of the learning. We aim at showing in this paper that
                 an improvement in population social diversity carried
                 out during the evolutionary",
  notes =        "FamilyTree, Statemachine, and Project Manager

                 Social semantic diversity instead of crowding
                 distance",
}

Genetic Programming entries for Edouard Batot Houari Sahraoui

Citations