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An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations

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Abstract

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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Authors and Affiliations

  1. Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    S. H. Ling & F. H. F. Leung

Authors
  1. S. H. Ling
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  2. F. H. F. Leung
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Ling, S.H., Leung, F.H.F. An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations. Soft Comput 11, 7–31 (2007). https://doi.org/10.1007/s00500-006-0049-7

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  • DOI: https://doi.org/10.1007/s00500-006-0049-7

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