Article ID: | iaor20042246 |
Country: | United Kingdom |
Volume: | 30 |
Issue: | 11 |
Start Page Number: | 1575 |
End Page Number: | 1593 |
Publication Date: | Sep 2003 |
Journal: | Computers and Operations Research |
Authors: | Sumichrast Robert T., Brown Evelyn C. |
Keywords: | genetic algorithms |
The grouping genetic algorithm (GGA), developed by Emmanuel Falkenauer, is a genetic algorithm whose encoding and operators are tailored to suit the special structure of grouping problems. In particular, the crossover operator for GGA involves the development of heuristic procedures to restore group membership to any entities that may have been displaced by preceding actions of the operator. In this paper, we present evidence that the success of a GGA is heavily dependent on the replacement heuristic used as a part of the crossover operator. We demonstrate this by comparing the performance of a GGA that uses a naïve replacement heuristic (GGA