Name
Title | ||
|---|---|---|
An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations |
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s00500-006-0049-7 | Soft Comput. |
Keywords | Field | DocType |
Crossover,Mutation,Real-coded genetic algorithm,Associative-memory neural network,Economic load dispatch | Mathematical optimization,Crossover,Suite,Computer science,Economic load dispatch,Artificial intelligence,Artificial neural network,Machine learning,Genetic algorithm,Wavelet | Journal |
Volume | Issue | ISSN |
11 | 1 | 1432-7643 |
Citations | PageRank | References |
43 | 3.04 | 16 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. H. Ling | 1 | 609 | 40.29 |
| F. H. F. Leung | 2 | 616 | 33.93 |