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

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

@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