Name
Abstract | ||
|---|---|---|
In many optimization problems, the structure of solutions reflects com- plex relationships between the different input parameters. For example, experience may tell us that certain parameters are closely related and should not be explored independently. Similarly, experience may estab- lish that a subset of parameters must take on particular values. Any search of the cost landscape should take advantage of these relationships. We present MIMIC, a framework in which we analyze the global structure of the optimization landscape. A novel and efficient algorithm for the estim- ation of this structure is derived. We use knowledge of this structure to guide a randomized search through the solution space and, in turn, to re- fine our estimate of the structure. Our technique obtains significant speed gains over other randomized optimization procedures. Advances in Neural Information Processing Systems 1997 MIT Press, Cambridge, MA |
Year | Venue | Keywords |
|---|---|---|
1996 | NIPS | information processing,optimization problem,random search,probability density |
Field | DocType | Citations |
Mathematical optimization,Global structure,Computer science,Artificial intelligence,Optimization problem,Machine learning | Conference | 220 |
PageRank | References | Authors |
17.65 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremy S. De Bonet | 1 | 612 | 122.12 |
| Charles L. Isbell | 2 | 504 | 65.79 |
| Paul Viola | 3 | 12742 | 1194.92 |