A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/eusflat/KoshiyamaVT15,
  author =       "Adriano Soares Koshiyama and 
                 Marley M. B. R. Vellasco and Ricardo Tanscheit",
  title =        "A Multi-gene Genetic Programming Fuzzy Inference
                 System for Regression Problems",
  booktitle =    "2015 Conference of the International Fuzzy Systems
                 Association and the European Society for Fuzzy Logic
                 and Technology ({IFSA}-{EUSFLAT}-15)",
  year =         "2015",
  editor =       "Jose M. Alonso and Humberto Bustince and 
                 Marek Reformat",
  address =      "Gijon, Spain",
  month =        jun # " 30-3 " # jul,
  publisher =    "Atlantis Press",
  keywords =     "genetic algorithms, genetic programming, genetic fuzzy
                 system, regression",
  bibdate =      "2015-11-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/eusflat/eusflat2015.html#KoshiyamaVT15",
  isbn13 =       "978-94-62520-77-6",
  URL =          "http://www.atlantis-press.com/php/download_paper.php?id=23616",
  DOI =          "doi:10.2991/ifsa-eusflat-15.2015.105",
  size =         "7 pages",
  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