An improved quantum genetic algorithm and its application

Authors:
Gexiang Zhang;Weidong Jin;Na Li
Affiliations:
School of Electrical Engineering, Southwest Jiaotong University, Sichuan, P.R.China;School of Electrical Engineering, Southwest Jiaotong University, Sichuan, P.R.China;School of Electrical Engineering, Southwest Jiaotong University, Sichuan, P.R.China
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

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Abstract

An improved quantum genetic algorithm (IQGA) is proposed in this paper. In IQGA, the strategies of updating quantum gate by using the best solution and introducing population catastrope are used. The typical function tests show convergent speed of IQGA is faster than that of quantum genetic algorithm (QGA) and other several GAs, and IQGA can also make up for prematureness of QGA. The simulations of FIR filter design demonstrate IQGA is superior to QGA, the methods in reference [5] and traditional method.