A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{conf/eusflat/KoshiyamaVT15,
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author = "Adriano Soares Koshiyama and
Marley M. B. R. Vellasco and Ricardo Tanscheit",
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title = "A Multi-gene Genetic Programming Fuzzy Inference
System for Regression Problems",
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booktitle = "2015 Conference of the International Fuzzy Systems
Association and the European Society for Fuzzy Logic
and Technology ({IFSA}-{EUSFLAT}-15)",
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year = "2015",
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editor = "Jose M. Alonso and Humberto Bustince and
Marek Reformat",
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address = "Gijon, Spain",
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month = jun # " 30-3 " # jul,
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publisher = "Atlantis Press",
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keywords = "genetic algorithms, genetic programming, genetic fuzzy
system, regression",
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bibdate = "2015-11-23",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eusflat/eusflat2015.html#KoshiyamaVT15",
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isbn13 = "978-94-62520-77-6",
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URL = "http://www.atlantis-press.com/php/download_paper.php?id=23616",
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DOI = "doi:10.2991/ifsa-eusflat-15.2015.105",
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size = "7 pages",
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abstract = "This work presents a novel Genetic Fuzzy System (GFS),
called Genetic Programming Fuzzy Inference System for
Regression problems (GPFIS-Regress). It makes use of
Multi-Gene Genetic Programming to build the premises of
fuzzy rules, including t-norms, negation and linguistic
hedge operators. GPFIS-Regress also defines a
consequent term that is more compatible with a given
premise and makes use of aggregation operators to weigh
fuzzy rules in accordance with their influence on the
problem. The system has been applied to a set of
benchmarks and has also been compared to other GFSs,
showing competitive results in terms of accuracy and
interpretability.",
- }
Genetic Programming entries for
Adriano Soares Koshiyama
Marley Maria Bernardes Rebuzzi Vellasco
Ricardo Tanscheit
Citations