Stopping Criteria for Finite Length Genetic Algorithms
INFORMS Journal on Computing · 1996 · Volume 8 · Issue 2
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.
Keywords
stochastic programming, computational complexity, analysis of algorithms
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