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Dynamical regimes and learning properties of evolved Boolean networks
2013
Neurocomputing
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
doi:10.1016/j.neucom.2012.05.023
fatcat:w3mqka3d3jexxel4rji57sd3su