Stopping criteria for finite length genetic algorithms
Document Type
Article
Publication Date
1-1-1996
Abstract
Considerable empirical results have been reported on the computational performance of genetic algorithms but little has been studied on their convergence behavior or on stopping criteria. In this paper we derive bounds on the number of iterations required to achieve a level of confidence to guarantee that a genetic algorithm has seen all populations and, hence, an optimal solution. © 1996 INFORMS.
Publication Title
INFORMS Journal on Computing
Recommended Citation
Aytug, H.,
&
Koehler, G.
(1996).
Stopping criteria for finite length genetic algorithms.
INFORMS Journal on Computing,
8(2), 183-191.
http://doi.org/10.1287/ijoc.8.2.183
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/13175