A Framework for Evolving Spiking Neural P Systems with Rules on Synapses

Abstract

In this paper, we present a genetic algorithm framework for evolving Spiking Neural P Systems with rules on synapses (RSSNP systems, for short). Starting with an initial RSSNP system, we use the genetic algorithm framework to obtain a derived RSSNP system with fewer resources (fewer and simpler rules, fewer synapses, less initial spikes) that can still produce the expected output spike trains. Different methods in the selection of parents and in the calculation of fitness are incorporated. We also try the framework on 5 RSSNP systems that compute bitwise AND, OR, NOT, ADD, and SUB respectively to gather data on how the framework behaves. Lastly, we discuss the asymptotic complexity of the algorithm and its effectiveness in generating fitter RSSNP systems based on which methods were used.