Genetic-Programming Approach to Learn Model Transformation Rules from Examples

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Martin Faunes and Houari A. Sahraoui and 
                 Mounir Boukadoum",
  title =        "Genetic-Programming Approach to Learn Model
                 Transformation Rules from Examples",
  booktitle =    "Proceedings of the 6th International Conference on
                 Theory and Practice of Model Transformations, ICMT
  year =         "2013",
  editor =       "Keith Duddy and Gerti Kappel",
  volume =       "7909",
  series =       "Lecture Notes in Computer Science",
  pages =        "17--32",
  address =      "Budapest, Hungary",
  month =        jun # " 18-19",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, JESS, ATL",
  isbn13 =       "978-3-642-38882-8",
  DOI =          "doi:10.1007/978-3-642-38883-5_2",
  bibdate =      "2013-06-15",
  bibsource =    "DBLP,
  size =         "16 pages",
  abstract =     "We propose a genetic programming-based approach to
                 automatically learn model transformation rules from
                 prior transformation pairs of source-target models used
                 as examples. Unlike current approaches, ours does not
                 need fine-grained transformation traces to produce
                 many-to-many rules. This makes it applicable to a wider
                 spectrum of transformation problems. Since the learnt
                 rules are produced directly in an actual transformation
                 language, they can be easily tested, improved and
                 reused. The proposed approach was successfully
                 evaluated on well-known transformation problems that
                 highlight three modelling aspects: structure, time
                 constraints, and nesting.",

Genetic Programming entries for Martin Faunes Houari Sahraoui Mounir Boukadoum