Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29275
Title: An improved genetic algorithm with average-bound crossover and wavelet mutation operations
Authors: Ling, SH
Leung, FHF 
Keywords: Associative-memory neural network
Crossover
Economic load dispatch
Mutation
Real-coded genetic algorithm
Issue Date: 2007
Publisher: Springer
Source: Soft computing, 2007, v. 11, no. 1, p. 7-31 How to cite?
Journal: Soft computing 
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.
URI: http://hdl.handle.net/10397/29275
ISSN: 1432-7643
DOI: 10.1007/s00500-006-0049-7
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