Dynamical regimes and learning properties of evolved Boolean networks

Boolean networks (BNs) have been mainly considered as genetic regulatory network models and are the subject of notable works in complex systems biology literature. Nevertheless, in spite of their similarities with neural networks, their potential as learning systems has not yet been fully investigated and exploited. In this work, we show that by employing metaheuristic methods we can train BNs to deal with two notable tasks, namely, the problem of controlling the BN's trajectory to match a set of requirements and the density classification problem. These tasks represent two important categories of problems in machine learning. The former is an example of the problems in which a dynamical system has to be designed such that its dynamics satisfies given requirements. The latter one is a representative task in classification. We also analyse the performance of the optimisation techniques as a function of the characteristics of the networks and the objective function and we show that the learning process could influence and be influenced by the BNs' dynamical condition.