Abstract
By introducing strong parallelism of quantum computing into evolutionary algorithm, a novel quantum genetic algorithm (NQGA) is proposed. In NQGA, a novel approach for updating the rotation angles of quantum logic gates and a strategy for enhancing search capability and avoiding premature convergence are adopted. Several typical complex continuous functions are chosen to test the performance of NQGA. Also, NQGA is applied in selecting the best feature subset from a large number of features in radar emitter signal recognition. The testing and experimental results of feature selection show that NQGA presents good search capability, rapid convergence, short computing time, and ability to avoid premature convergence effectively.
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