Name
Abstract | ||
|---|---|---|
In this work we present a simple state-of-the-art selection hyperheuristic called Fair-Share Iterated Local Search (FS-ILS). FS-ILS is an iterated local search method using a conservative restart condition. Each iteration, a perturbation heuristic is selected proportionally to the acceptance rate of its previously proposed candidate solutions (after iterative improvement) by a domain-independent variant of the Metropolis condition. FS-ILS was developed in prior work using a semi-automated design approach. That work focused on how the method was found, rather than the method itself. As a result, it lacked a detailed explanation and analysis of the method, which will be the main contribution of this work. In our experiments we analyze FS-ILS's parameter sensitivity, accidental complexity and compare it to the contestants of the CHeSC (2011) competition. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2576768.2598285 | GECCO |
Keywords | Field | DocType |
combinatorial optimization,hyperheuristics,optimization | Mathematical optimization,Heuristic,Computer science,Combinatorial optimization,Acceptance rate,Artificial intelligence,Machine learning,Iterated local search | Conference |
Citations | PageRank | References |
4 | 0.43 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven Adriaensen | 1 | 15 | 2.44 |
| Tim Brys | 2 | 95 | 11.90 |
| Ann Nowé | 3 | 971 | 123.04 |