Abstract
Among the many applications of tness landscape analysis a prominent
example is algorithm selection. e no-free-lunch (NFL) theorem
has put a limit on the general applicability of heuristic search
methods. Improved methods can only be found by specialization
to certain problem characteristics which limits their application
to other problems. is creates a very interesting and dynamic
eld for algorithm development. However, this also leads to the
denition of a large range of dierent algorithms that are hard
to compare exhaustively. An additional challenge is posed by the
fact that algorithms have parameters and thus to each algorithm
there may be a large number of instances. In this work the application
of algorithm selection to problem instances of the quadratic
assignment problem (QAP) is discussed.
| Original language | English |
|---|---|
| Title of host publication | GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany, Deutschland, 2017 |
| Number of pages | 8 |
| Publication status | Published - 2017 |
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