Genetic-Programming Approach to Learn Model Transformation Rules from Examples

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/icmt/FaunesSB13,

• 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 2013",
• 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, http://dblp.uni-trier.de/db/conf/icmt/icmt2013.html#FaunesSB13",
• 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

Citations