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 …

The importance of calibration of microscopic traffic models as the main core of traffic
simulation software results from the need for more realistic traffic behaviors. The latent …

This paper proposes a genetic algorithm (GA) combined with ray tracer to generate a cell-
free topology of massive MIMO (mMIMO) for the optimal focusing performance serving …

Abstract This paper describes Mateda-2.0, a MATLAB package for estimation of distribution
algorithms (EDAs). This package can be used to solve single and multi-objective discrete …

This paper introduces exact learning of Bayesian networks in estimation of distribution
algorithms. The estimation of Bayesian network algorithm (EBNA) is used to analyze the …

The estimation of distribution algorithm (EDA) is a new evolutionary algorithm developed as
an alternative to the traditional genetic algorithm (GA). The EDA guides the search by …

Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not
much information available. From this standpoint, estimation of distribution algorithms …

This paper presents a new grammatical evolution (GE) that generates automatic program
under favor of probabilistic context-free grammar. A population of individuals is evolved …

In this paper, we identify a number of topics relevant for the improvement and development
of discrete estimation of distribution algorithms. Focusing on the role of probability …

The successful application of estimation of distribution algorithms (EDAs) to solve different
kinds of problems has reinforced their candidature as promising black-box optimization …