Thanks to their inherent properties, probabilistic graphical models are one of the prime
candidates for machine learning and decision making tasks especially in uncertain domains …

Automated techniques for Arabic content recognition are at a beginning period contrasted
with their partners for the Latin and Chinese contents recognition. There is a bulk of …

The field of natural computing has been the focus of a substantial research effort in recent
decades. One particular strand of this concerns the development of computational …

Optimization problems are ubiquitous in every field, and they are becoming more and more
complex, which greatly challenges the effectiveness of existing optimization methods. To …

In many engineering applications it is common to find optimization problems where the cost
function and/or constraints require complex simulations. Though it is often, but not always …

Since estimation of distribution algorithms (EDAs) were proposed, many attempts have been
made to improve EDAs' performance in the context of global optimization. So far, the studies …

Traditional estimation of distribution algorithms (EDAs) often use Gaussian densities to
optimize continuous functions, such as the estimation of Gaussian network algorithms …

As a representative evolutionary algorithm based on probabilistic models, the estimation of
distribution algorithm (EDA) is widely applied in complex continuous optimization problems …

The meta-heuristics is an effective way to solve the complex optimization problems.
However, the applicability of meta-heuristic is restricted in real applications due to the …

As a typical model-based evolutionary algorithm, estimation of distribution algorithm (EDA)
possesses unique characteristics and has been widely applied in global optimization …