Evolutionary methods for tuning a robot sound recognition system

Abstract

A spatially-dispersed GA with co-evolutionary methodology was developed to artificially evolve temporal-parameters for a spiking neural-model of the cricket auditory system capable of performing phonotaxis. Male chromosomes containing genes that encode for the temporal properties of calling songs were simultaneously evolved in the co-evolutionary model. The application of A.I. modelling to the evolution of cricket species and their mating behaviour is reviewed. The GA model produced discrete spatial groupings of individuals, which had distinct genetic code within the male and female chromosomes. Networks with neural-parameters set by the female chromosome’s genes showed a higher phonotactic performance when responding to songs produced by males within that group than to songs produced by males from other groups, supporting conspecific preference of calling song. However, this effect varied greatly between groups and trials. The algorithm’s behaviour is complex, dynamic and chaotic, with highly dimensional data necessitating complex analysis. The resulting analysis does not provide a clear or concise synopsis of the behaviour and has left some open questions that would require further research.