We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation...
The paper introduces mixed networks, a new framework for expressing and reasoning with probabilistic and deterministic information. The framework combines belief networks with con...
Queueing network models have been extensively applied to represent and analyze resource sharing systems such as communication and computer systems and they have proved to be a pow...
We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a...
In autonomic networks, the self-configuration of network entities is one of the most desirable properties. In this paper, we show how formal verification techniques can verify the ...