Title :
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
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;
Conference_Titel :
IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint
Conference_Location :
Edmonton, AB
DOI :
10.1109/IFSA-NAFIPS.2013.6608370
