A quantum-inspired evolutionary algorithm for fuzzy classification

Author

Nunes, Wesley ; Vellasco, Marley ; Tanscheit, Ricardo

Author_Institution

Dept. of Electr. Eng., Pontifical Catholic Univ. of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil

fYear

2013

fDate

24-28 June 2013

Firstpage

29

Lastpage

34

Abstract

This paper presents a new optimization algorithm based on quantum-inspired evolutionary techniques that simultaneously incorporates two important features: (i) the treatment of multiple objectives and (ii) the treatment of related categorical attributes, applicable to a specific form of combinatorial optimization. The proposed optimization algorithm is applied to the development of fuzzy inference systems for classification, seeking to achieve the goals of maximum efficiency ratings and high level of system interpretability.

Keywords

evolutionary computation; fuzzy reasoning; fuzzy set theory; pattern classification; quantum computing; categorical attributes; fuzzy classification; fuzzy inference systems; maximum efficiency ratings; optimization algorithm; quantum-inspired evolutionary algorithm; system interpretability; Classification algorithms; Fuzzy logic; Genetic algorithms; Input variables; Optimization; Sociology; Statistics; Fuzzy systems; Genetic fuzzy systems; Quantum inspired genetic algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint

Conference_Location

Edmonton, AB

Type

conf

DOI

10.1109/IFSA-NAFIPS.2013.6608370

Filename

6608370