A compact evolutionary algorithm for integer spiking neural network robot controllers

Abstract

In order to facilitate online training of a robot controller composed of a spiking neural network, we propose the creation of a method dubbed a `compact evolutionary algorithm'. The compact evolutionary algorithm, derived from the compact genetic algorithm, greatly reduces the memory requirements for evolutionary optimization and also obviates the need for floating-point arithmetic capabilities allowing its efficient implementation by microcontrollers. The compact evolutionary algorithm is compared to the traditional evolutionary algorithm for solving three cyclic functions that are of use in a walking robot.