A comparative study of various evolutionary algorithms used for fuzzy rule-based inference and learning systems

Zsolt Dányádi, Krisztián Balázs, L. Kóczy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The goal of this paper is to provide an overview of a variety of evolutionary algorithms, comparing their efficiency on fuzzy rule-based inference and learning. Fuzzy rule-based inference can be used to model a desirable outward behavior of a system when given a specific input, which, in the case of this comparative study, is determined by a set of samples, generated by sufficiently complex objective functions. Optimizing a fuzzy rule-based inference system is a matter of finding a rule base that is as close to imitating the desired behavior as possible. While the specific applications of evolutionary methods are endless, the objective functions used here remain general in nature.

Original languageEnglish
Title of host publicationICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Proceedings
Pages49-54
Number of pages6
DOIs
Publication statusPublished - 2010
EventIEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, ICCC-CONTI 2010 - Timisoara
Duration: May 27 2010May 29 2010

Other

OtherIEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, ICCC-CONTI 2010
CityTimisoara
Period5/27/105/29/10

Fingerprint

Fuzzy rules
Evolutionary algorithms
Learning systems

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Information Systems

Cite this

Dányádi, Z., Balázs, K., & Kóczy, L. (2010). A comparative study of various evolutionary algorithms used for fuzzy rule-based inference and learning systems. In ICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Proceedings (pp. 49-54). [5491228] https://doi.org/10.1109/ICCCYB.2010.5491228

A comparative study of various evolutionary algorithms used for fuzzy rule-based inference and learning systems. / Dányádi, Zsolt; Balázs, Krisztián; Kóczy, L.

ICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Proceedings. 2010. p. 49-54 5491228.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Dányádi, Z, Balázs, K & Kóczy, L 2010, A comparative study of various evolutionary algorithms used for fuzzy rule-based inference and learning systems. in ICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Proceedings., 5491228, pp. 49-54, IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, ICCC-CONTI 2010, Timisoara, 5/27/10. https://doi.org/10.1109/ICCCYB.2010.5491228
Dányádi Z, Balázs K, Kóczy L. A comparative study of various evolutionary algorithms used for fuzzy rule-based inference and learning systems. In ICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Proceedings. 2010. p. 49-54. 5491228 https://doi.org/10.1109/ICCCYB.2010.5491228
Dányádi, Zsolt ; Balázs, Krisztián ; Kóczy, L. / A comparative study of various evolutionary algorithms used for fuzzy rule-based inference and learning systems. ICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Proceedings. 2010. pp. 49-54
@inproceedings{bd6082b983eb4c378366cca4fc35dcc2,
title = "A comparative study of various evolutionary algorithms used for fuzzy rule-based inference and learning systems",
abstract = "The goal of this paper is to provide an overview of a variety of evolutionary algorithms, comparing their efficiency on fuzzy rule-based inference and learning. Fuzzy rule-based inference can be used to model a desirable outward behavior of a system when given a specific input, which, in the case of this comparative study, is determined by a set of samples, generated by sufficiently complex objective functions. Optimizing a fuzzy rule-based inference system is a matter of finding a rule base that is as close to imitating the desired behavior as possible. While the specific applications of evolutionary methods are endless, the objective functions used here remain general in nature.",
author = "Zsolt D{\'a}ny{\'a}di and Kriszti{\'a}n Bal{\'a}zs and L. K{\'o}czy",
year = "2010",
doi = "10.1109/ICCCYB.2010.5491228",
language = "English",
isbn = "9781424474332",
pages = "49--54",
booktitle = "ICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Proceedings",

}

TY - GEN

T1 - A comparative study of various evolutionary algorithms used for fuzzy rule-based inference and learning systems

AU - Dányádi, Zsolt

AU - Balázs, Krisztián

AU - Kóczy, L.

PY - 2010

Y1 - 2010

N2 - The goal of this paper is to provide an overview of a variety of evolutionary algorithms, comparing their efficiency on fuzzy rule-based inference and learning. Fuzzy rule-based inference can be used to model a desirable outward behavior of a system when given a specific input, which, in the case of this comparative study, is determined by a set of samples, generated by sufficiently complex objective functions. Optimizing a fuzzy rule-based inference system is a matter of finding a rule base that is as close to imitating the desired behavior as possible. While the specific applications of evolutionary methods are endless, the objective functions used here remain general in nature.

AB - The goal of this paper is to provide an overview of a variety of evolutionary algorithms, comparing their efficiency on fuzzy rule-based inference and learning. Fuzzy rule-based inference can be used to model a desirable outward behavior of a system when given a specific input, which, in the case of this comparative study, is determined by a set of samples, generated by sufficiently complex objective functions. Optimizing a fuzzy rule-based inference system is a matter of finding a rule base that is as close to imitating the desired behavior as possible. While the specific applications of evolutionary methods are endless, the objective functions used here remain general in nature.

UR - http://www.scopus.com/inward/record.url?scp=77955169906&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77955169906&partnerID=8YFLogxK

U2 - 10.1109/ICCCYB.2010.5491228

DO - 10.1109/ICCCYB.2010.5491228

M3 - Conference contribution

AN - SCOPUS:77955169906

SN - 9781424474332

SP - 49

EP - 54

BT - ICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Proceedings

ER -