Article ID: | iaor20062222 |
Country: | Netherlands |
Volume: | 166 |
Issue: | 1 |
Start Page Number: | 51 |
End Page Number: | 62 |
Publication Date: | Oct 2005 |
Journal: | European Journal of Operational Research |
Authors: | Mastrolilli Monaldo, Bianchi Leonora |
Keywords: | scheduling, optimization |
Data generation for computational testing of optimization algorithms is a key topic in experimental algorithmics. Recently, concern has arisen that many published computational experiments are inadequate with respect to the way test instances are generated. In this paper we suggest a new research direction that might be useful to cope with the possible limitations of data generation. The basic idea is to select a finite set of instances which ‘represent’ the whole set of instances. We propose a measure of the representativeness of an instance, which we call ε-representativeness: for a minimization problem, an instance x