Quantum Probability Coding Genetic Algorithm and Its Applications

Li Bin; Tan Li-xiang; Zou Yi; Zhuang Zhen-quan

Volume 27 Issue 5

May 2005

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Li Bin, Tan Li-xiang, Zou Yi, Zhuang Zhen-quan . Quantum Probability Coding Genetic Algorithm and Its Applications[J]. Journal of Electronics & Information Technology, 2005, 27(5): 805-810.

Citation:

Li Bin, Tan Li-xiang, Zou Yi, Zhuang Zhen-quan . Quantum Probability Coding Genetic Algorithm and Its Applications[J]. Journal of Electronics & Information Technology, 2005, 27(5): 805-810.

Li Bin, Tan Li-xiang, Zou Yi, Zhuang Zhen-quan . Quantum Probability Coding Genetic Algorithm and Its Applications[J]. Journal of Electronics & Information Technology, 2005, 27(5): 805-810.

Citation:

Li Bin, Tan Li-xiang, Zou Yi, Zhuang Zhen-quan . Quantum Probability Coding Genetic Algorithm and Its Applications[J]. Journal of Electronics & Information Technology, 2005, 27(5): 805-810.

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Quantum Probability Coding Genetic Algorithm and Its Applications

Received Date: 2003-12-17
Rev Recd Date: 2004-07-26
Publish Date: 2005-05-19

Abstract

Abstract

A Quantum probability Coding Genetic Algorithm-QCGA is proposed, which is different from classical GAs. In QCGA, single individual represents a probability distribution of solutions, which covers the whole solution space. Individuals in QCGA evolve independently and in parallel. A new crossover operator is designed to implement the information exchange among individuals. A new mutation operator is also design to prevent the algorithm from falling into local optima. To study the efficiency and advantage of QCGA, the algorithm is applied to solve function optimization problems, knapsack problems, and to discover frequent structures from time series. Experimental results show that QCGA has good ability of global optimization, and good ability of diversity reservation, which makes it efficient for complex optimization problems.

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References(1)

References

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