Name
Abstract | ||
|---|---|---|
This paper proposes a model for metaheuristic research which recognises the need to match algorithms to problems. An empirical approach to producing a mapping from problems to algorithms is presented. This mapping, if successful, will encapsulate the knowledge gained from the application of metaheuristics to the spectrum of real problems. Information theoretic measures are suggested as a means of associating a dominant algorithm with a set of problems. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-89694-4_33 | SEAL |
Keywords | Field | DocType |
Metaheuristics,information theory,optimisation,problem classification | Information theory,Mathematical optimization,Parallel metaheuristic,Computer science,Artificial intelligence,Match algorithms,Machine learning,Metaheuristic | Conference |
Volume | ISSN | Citations |
5361 | 0302-9743 | 8 |
PageRank | References | Authors |
0.53 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kent C. B. Steer | 1 | 12 | 1.74 |
| A. Wirth | 2 | 164 | 15.46 |
| Saman K. Halgamuge | 3 | 745 | 93.40 |