On the optimal convergence probability of univariate estimation of distribution algorithms
R Rastegar - Evolutionary computation, 2011 - ieeexplore.ieee.org
Evolutionary computation, 2011•ieeexplore.ieee.org
In this paper we obtain bounds on the probability of convergence to the optimal solution for
the compact genetic algorithm (cGA) and the population based incremental learning (PBIL).
Moreover, we give a sufficient condition for convergence of these algorithms to the optimal
solution and compute a range of possible values for algorithm parameters at which there is
convergence to the optimal solution with a predefined confidence level.
the compact genetic algorithm (cGA) and the population based incremental learning (PBIL).
Moreover, we give a sufficient condition for convergence of these algorithms to the optimal
solution and compute a range of possible values for algorithm parameters at which there is
convergence to the optimal solution with a predefined confidence level.
In this paper we obtain bounds on the probability of convergence to the optimal solution for the compact genetic algorithm (cGA) and the population based incremental learning (PBIL). Moreover, we give a sufficient condition for convergence of these algorithms to the optimal solution and compute a range of possible values for algorithm parameters at which there is convergence to the optimal solution with a predefined confidence level.
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