Biologically inspired motion detection neural network models evolved using genetic algorithms

In this paper we describe a method to evolve biologically inspired motion detection systems utilizing artificial neural networks (ANN's). Previously, the evolution of neural networks has focused on feed-forward neural networks or networks with predefined architectures. The purpose of this paper is to present a novel method for evolving neural networks with no predefined architectures to solve various problems including motion detection models. The neural network models are evolved with genetic algorithms using an encoding that defines a functional network with no restriction on recurrence, activation function types, or the number of nodes that compose the final ANN. The genetic algorithm operates on a population of potential solutions where each potential network is represented in a chromosome. The structure of each chromosome in the population is defined with a weight matrix which allows for efficient simulation of outputs. Each chromosome is evaluated by a fitness function that scores how well the actual output of an ANN compares to the expected output. Crossovers and mutations are made with specified probabilities between population members to evolve new members of the population. After a number of iterations a near optimal network is evolved that solves the problem at hand. The approach has proven to be sufficient to create biologically realistic motion detection neural network models with results that are comparable to results obtained from the standard Reichardt model.