An evolutionary approach to the design of spiking neural P circuits

In this paper, we define spiking neural P circuits (SN P circuits) as an acyclic variant of spiking neural P systems. We then study how well genetic algorithms (GA) are able to find an SN P circuit that computes a given Boolean function, possibly partially defined. The proposed technique can be used to find SN P circuits that solve binary classification problems. We performed several computer experiments, testing different mutation operators and several combinations of hyperparameter values. The preliminary results obtained show that the probability of success of GA strongly depends upon the structure (in particular, the algebraic degree and the number of input/output variables) of the Boolean function to be computed.