Truncation Selection and Gaussian EDA: Bounds for Sustainable Progress in High-Dimensional Spaces

In real-valued estimation-of-distribution algorithms, the Gaussian distribution is often used along with maximum likelihood (ML) estimation of its parameters. Such a process is highly prone to premature convergence. The simplest method for preventing premature convergence of Gaussian distribution is enlarging the maximum likelihood estimate of σ by a constant factor k each generation. Such a factor should be large enough to prevent convergence on slopes of the fitness function, but should not be too large to allow the algorithm converge in the neigh- borhood of the optimum. Previous work showed that for truncation se- lection such admissible k exists in 1D case. In this article it is shown experimentaly, that for the Gaussian EDA with truncation selection in high-dimensional spaces no admissible k exists!