Convergence analysis of adaptive genetic algorithms
Yijia Cao · 1997
Crossover and mutation play an important role in genetic search and adaptive crossover and mutation operators have been employed to improve performance of genetic algorithms (GAs). In this paper, a nonstationary Markov model is developed to investigate asymptotic convergence properties of the adaptive genetic algorithms (AGAs). It is shown that in many cases, AGAs would asymptotically converge. But there do exist some situations that AGAs conditionally converge.