摘要 :
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 strateg...
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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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摘要 :
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 ...
展开
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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摘要 :
The greedy algorithm is a strong local searching algorithm. The genetica lgorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy ...
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The greedy algorithm is a strong local searching algorithm. The genetica lgorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy genetic algorithm which incorporates the global exploring ability of the genetic algorithm and the local convergent ability of the greedy algorithm. Experimental results show that greedy genetic algorithm gives much better results than the classical genetic algorithm.
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摘要 :
The accuracy of numerical simulations and many other material design calculations, such as the rolling force, rollingtorque, etc., depends on the description of stress-strain relationship of the deformed materials. One common meth...
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The accuracy of numerical simulations and many other material design calculations, such as the rolling force, rollingtorque, etc., depends on the description of stress-strain relationship of the deformed materials. One common methodof describing the stres
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摘要 :
Distributed genetic algorithm can be combined with the adaptive genetic algorithm for mining the interesting and comprehensible classification rules. The paper gives the method to encode for the rules, the fitness function, the se...
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Distributed genetic algorithm can be combined with the adaptive genetic algorithm for mining the interesting and comprehensible classification rules. The paper gives the method to encode for the rules, the fitness function, the selecting, crossover, mutation and migration operator for the DAGA at the same time are designed.
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摘要 :
Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted fro...
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Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted from user's preference mechanisms. Characteristics of the two noises are presented aiming at the application of interactive genetic algorithms in dealing with images. The evolutionary phases of interactive genetic algorithms are determined according to differences in the same individual's fitness among different generations. Models for noises in different phases are established and the corresponding strategies for reducing noises are given. The algorithm proposed in this paper has been applied to fashion design, which is a typical example of image processing. The results show that the strategies can reduce noises in interactive genetic algorithms and improve the algorithm's performance effectively. However, a further study is needed to solve the problem of determining the evolution phase by using suitable objective methods so as to find out an effective method to decrease noises.
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