Synergies between evolutionary computation and probabilistic graphical models

A review on probabilistic graphical models in evolutionary computation

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. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithm...

Synergies between Evolutionary and Neural Computation

Evolutionary and neural computation share the same philosophy to use biological information processing for the solution of technical problems. Besides this important but rather abstract common foundation, there have also been many successful combinations of both methods for solving problems as applied as the design of turbomachinery components. In this paper, we will introduce evolutionary algo...

Probabilistic Graphical Models

P Probabilistic Graphical Models

Bayesian network (BN) A directed graph whose nodes represent variables, and edges represent influences. Together with conditional probability distributions, a Bayesian network represents the joint probability distribution of its variables. Conditional probability distribution Assignment of probabilities to all instances of a set of variables when the value of one or more variables is known. Con...

Visualizing Graphical Probabilistic Models

Complex probabilistic models are difficult to evaluate not only for consistency with the domain theory but also for novelty and significance. In this paper we describe the current visual representations of Bayesian networks and then present an implementation containing enhancements to these visualizations by integrating several well known techniques. We then apply our approach in an exploration...