An improved genetic algorithm with average-bound crossover and wavelet mutation operations
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作者:
Ling, S. H.
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Hong Kong Polytech Univ, Dept Elect & Informat Engn, Ctr Multimedia Signal Proc, Kowloon, Hong Kong, Peoples R ChinaHong Kong Polytech Univ, Dept Elect & Informat Engn, Ctr Multimedia Signal Proc, Kowloon, Hong Kong, Peoples R China
Ling, S. H.
[1
]
Leung, F. H. F.
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Hong Kong Polytech Univ, Dept Elect & Informat Engn, Ctr Multimedia Signal Proc, Kowloon, Hong Kong, Peoples R ChinaHong Kong Polytech Univ, Dept Elect & Informat Engn, Ctr Multimedia Signal Proc, Kowloon, Hong Kong, Peoples R China
Leung, F. H. F.
[1
]
机构:
[1] Hong Kong Polytech Univ, Dept Elect & Informat Engn, Ctr Multimedia Signal Proc, Kowloon, Hong Kong, Peoples R China
This paper presents a real-coded genetic algorithm (RCGA) with new genetic operations (crossover and mutation). They are called the average-bound crossover and wavelet mutation. By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. Application examples on economic load dispatch and tuning an associative-memory neural network are used to show the performance of the proposed RCGA.
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页码:7 / 31
页数:25
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