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 language | English |
|---|---|
| Title of host publication | ICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Proceedings |
| Pages | 49-54 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, ICCC-CONTI 2010 - Timisoara Duration: May 27 2010 → May 29 2010 |
Other
| Other | IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, ICCC-CONTI 2010 |
|---|---|
| City | Timisoara |
| Period | 5/27/10 → 5/29/10 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computational Theory and Mathematics
- Information Systems
Cite this
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 proceeding › Conference contribution
}
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 -