- 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",
