Generating model transformation rules from examples using an evolutionary algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Faunes:2012:ASE,

• author = "Martin Faunes and Houari Sahraoui and Mounir Boukadoum",
• title = "Generating model transformation rules from examples using an evolutionary algorithm",
• booktitle = "Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, ASE 2012",
• year = "2012",
• editor = "Tim Menzies and Motoshi Saeki",
• pages = "250--253",
• address = "Essen, Germany",
• publisher_address = "New York, NY, USA",
• month = "3-7 " # sep,
• publisher = "ACM",
• keywords = "genetic algorithms, genetic programming, Model transformation by example",
• isbn13 = "978-1-4503-1204-2",
• URL = "http://doi.acm.org/10.1145/2351676.2351714",
• DOI = "doi:10.1145/2351676.2351714",
• acmid = "2351714",
• size = "4 pages",
• abstract = "We propose an evolutionary approach to automatically generate model transformation rules from a set of examples. To this end, genetic programming is adapted to the problem of model transformation in the presence of complex input/output relationships (i.e., models conforming to meta-models) by generating declarative programs (i.e., transformation rules in this case). Our approach does not rely on prior transformation traces for the model-example pairs, and directly generates executable, many-to-many rules with complex conditions. The applicability of the approach is illustrated with the well-known problem of transforming UML class diagrams into relational schema, using examples collected from the literature.",
• notes = "http://ase2012.paluno.uni-due.de/",

}

Genetic Programming entries for Martin Faunes Houari Sahraoui Mounir Boukadoum

Citations