A Novel Parent Selection Operator in GA for Tuning of Scaling Factors of FKBC

Author

Azeem, Mohammad Fazle

Author_Institution

Aligarh Muslim Univ., Aligarh

fYear

0

fDate

0-0 0

Firstpage

1742

Lastpage

1747

Abstract

An exhaustive list that encompasses a wide range of combination for genetic algorithm (GA) operators exist in the literature. Most of them have been applied on different type of tuning application for fuzzy knowledge base controller (FKBC). In this paper author has proposed a modification to the Sung\´s [Sung Hoon Jung, "Queen bee evolution for genetic algorithms", Electronics letters, 20th March 2003, Vol.36 No. 6 pp. 575-576] GA. The proposed GA utilizes the weighted crossover operator. A fitness function, which guides the evolution process, is defined as inverse of integral time absolute error (ITAE). The proposed method is applied, for the tuning of input and output scaling factors of FKBC, on four different types of complex non-linear systems. The simulation results are encouraging.

Keywords

fuzzy control; fuzzy logic; genetic algorithms; knowledge based systems; nonlinear systems; complex nonlinear system; fitness function; fuzzy knowledge base controller; fuzzy logic control; genetic algorithm operator; input scaling factor; integral time absolute error; novel parent selection operator; output scaling factor; weighted crossover operator; Control systems; Decision making; Error correction; Fuzzy control; Fuzzy logic; Fuzzy sets; Fuzzy systems; Genetic algorithms; Humans; Mathematical analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2006 IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9488-7

Type

conf

DOI

10.1109/FUZZY.2006.1681941

Filename

1681941