Abstract

Quantum genetic algorithm (QGA) is firstly improved for numerical optimization with real coding, where populations are updated by a simple rotation method which inspires a real quantum genetic algorithm (RQGA), then simulated annealing (SA) is reasonably introduced in the optimizing process of RQGA, and a hybrid quantum genetic algorithm (HQGA) is presented, which could not only effectively avoid the premature phenomenon but also accelerate the search efficiency under the introduction of SA. Besides HQGA is applied to numerical optimization and the training of BP neural network, and through a comparison among QGA, RQGA and HQGA, it is obviously shown that HQGA performs better on running speed and optimizing capability.

Key words: Quantum genetic algorithm simulated annealing, hybrid algorithm, real coding.