Artificial ontogenesis of controllers for robotic behavior using VLG GA

In this paper we describe a method for the synthesis of robotic controllers using evolutionary techniques. A modified version of the recurrent neural network used for controlling the robots is evolved using genetic algorithm using the variable length genotype approach where the genotype encodes the network. It has been discovered that the separation of modalities in the network like vision and touch, for the first few layers helps in faster evolution as well as in the development of faster controller networks. The structure of the network that emerges from this kind of evolution is similar to brain-like networks. Performance of plastic versus non-plastic individuals has also been explored. Gene-blocking technique has been used for developing several behaviors in the same evolution cycle. The final controller developed at the end of the evolutionary process was tested on a Khepera.