Abstract
Algorithm selection is useful in decision situations where among many alternative algorithm instances one has to be chosen. This is often the case in heuristic optimization and is detailed by the well-known no-free-lunch (NFL) theorem. A consequence of the NFL is that a heuristic algorithm may only gain a performance improvement in a subset of the problems. With the present study we aim to identify correlations between observed differences in performance and problem characteristics obtained from statistical analysis of the problem instance and from fitness landscape analysis (FLA). Finally we evaluate the performance of a recommendation algorithm that uses this information to make an informed choice for a certain algorithm instance.
| Original language | English |
|---|---|
| Title of host publication | GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference |
| Number of pages | 8 |
| Publication status | Published - 2018 |
Fields of science
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102011 Formal languages
- 102022 Software development
- 102031 Theoretical computer science
- 603109 Logic
- 202006 Computer hardware
JKU Focus areas
- Computation in Informatics and Mathematics