An improved genetic algorithm with average-bound crossover and wavelet mutation operations
- Publication Type:
- Journal Article
- Soft Computing, 2007, 11 (1), pp. 7 - 31
- Issue Date:
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. © Springer-Verlag 2006.
Please use this identifier to cite or link to this item: