Evolutionary Bayesian Network Design for High Dimensional Experiments

Laboratory experimentation is increasingly concerned with systems whose dynamical behaviour can be affected by a very large number of variables. Objectives of experimentation on such systems are generally both the optimisation of some experimental responses and efficiency of experimentation in terms of low investment of resources and low impact on the environment. Design and modelling for high dimensional systems with these objectives present hard and challenging problems, to which much current research is devoted. In this paper, we introduce a novel approach based on the evolutionary principle and Bayesian network models. This approach can discover optimum values while testing just a very limited number of experimental points. The very good performance of the approach is shown both in a simulation analysis and biochemical study concerning the emergence of new functional bio-entities. © 2014 Elsevier B.V.

Laboratory experimentation is increasingly concerned with systems whose dynamical behaviour can be affected by a very large number of variables. Objectives of experimentation on such systems are generally both the optimisation of some experimental responses and efficiency of experimentation in terms of low investment of resources and low impact on the environment. Design and modelling for high dimensional systems with these objectives present hard and challenging problems, to which much current research is devoted. In this paper, we introduce a novel approach based on the evolutionary principle and Bayesian network models. This approach can discover optimum values while testing just a very limited number of experimental points. The very good performance of the approach is shown both in a simulation analysis and biochemical study concerning the emergence of new functional bio-entities.