Article ID: | iaor19981971 |
Country: | Japan |
Volume: | 33 |
Issue: | 9 |
Start Page Number: | 939 |
End Page Number: | 946 |
Publication Date: | Sep 1997 |
Journal: | Transactions of the Society of Instrument and Control Engineers |
Authors: | Tamaki Hisashi, Maekawa Keiji, Mori Naoki, Kita Hajime, Yoshikazu Nishikawa |
Keywords: | heuristics, programming: integer |
For successful applications of the genetic algorithm, there are two important points to be considered. The first point is the design of the fitness landscape introduced by the representation of the solution as a chromosome and searching operations such as crossover and mutation. The second is control of the convergence brought about by the selection operation. In the conventional implementation of GA, these two points are mutually dependent, i.e., a suitable selection pressure varies largely depending on, e.g., the crossover operator. Hence, it requires much trial-and-error effort to find a nice configuration of GA. In the present paper, the authors apply a novel selection rule, the Thermodynamical Genetic Algorithm (TDGA) proposed by Mori