Name
Abstract | ||
|---|---|---|
We are developing GA-based tools for use in constructing high quality approximations to continuous functions. In this paper we report on a GA-based method for adaptively select-ing e ective interpolation points. We evalu-ate our approach on a variety of test func-tions, and we compare are results to more traditional approaches. The results are quite promising and suggest directions for further improvements. |
Year | Venue | Keywords |
|---|---|---|
2000 | GECCO | function approximation |
Field | DocType | Citations |
Continuous function,Adaptive interpolation,Mathematical optimization,Function approximation,Computer science,Interpolation,Artificial intelligence,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rida E. Moustafa | 1 | 3 | 1.25 |
| Kenneth De Jong | 2 | 3798 | 525.78 |
| Edward J. Wegman | 3 | 36 | 7.84 |