Genetic-Programming Approach to Learn Model Transformation Rules from Examples
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{conf/icmt/FaunesSB13,
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author = "Martin Faunes and Houari A. Sahraoui and
Mounir Boukadoum",
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title = "Genetic-Programming Approach to Learn Model
Transformation Rules from Examples",
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booktitle = "Proceedings of the 6th International Conference on
Theory and Practice of Model Transformations, ICMT
2013",
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year = "2013",
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editor = "Keith Duddy and Gerti Kappel",
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volume = "7909",
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series = "Lecture Notes in Computer Science",
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pages = "17--32",
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address = "Budapest, Hungary",
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month = jun # " 18-19",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, JESS, ATL",
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isbn13 = "978-3-642-38882-8",
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DOI = "doi:10.1007/978-3-642-38883-5_2",
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bibdate = "2013-06-15",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icmt/icmt2013.html#FaunesSB13",
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size = "16 pages",
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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