Name
Abstract | ||
|---|---|---|
An algorithm for adaptively controlling genetic algorithm parameter (GAP) coding using fuzzy rules is presented. The fuzzy GAP coding algorithm is compared to the dynamic parameter encoding scheme proposed by Schraudolph and Belew. The performance of the algorithm on a hydraulic brake emulator parameter identification problem is investigated. Fuzzy GAP coding control is shown to dramatically increase the rate of convergence and accuracy of genetic algorithms |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/3477.764878 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
clustering algorithms,genetic algorithms,dynamic range,parameter estimation,genetic algorithm,rate of convergence,encoding,fuzzy control,convergence,neural networks | Control theory,Computer science,Fuzzy logic,Coding (social sciences),Artificial intelligence,Rate of convergence,Fuzzy control system,Cluster analysis,Population-based incremental learning,Parameter identification problem,Genetic algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
29 | 3 | 1083-4419 |
Citations | PageRank | References |
7 | 0.80 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert J. Streifel | 1 | 7 | 0.80 |
| Robert J. Marks | 2 | 254 | 55.90 |
| Russell Reed | 3 | 7 | 0.80 |
| Jai J. Choi | 4 | 7 | 0.80 |
| michael j healy | 5 | 10 | 1.41 |