In our model the structure of the network as well as the sensoric part of the robot are encoded in the genome so that they can be optimised simultaneously. We take neural networks as a basic structure, because they are robust against noise and their natural counterpart is a product of evolution. The task of the robot is to find short paths from defined starting positions to different goals. We simulate several environments in order to avoid a specialisation of the robot on a single environment. The idea is that evolving the networks in a variety of environments, a class of them can be learned.
Parameters contain information about a class of environments rather than about a single environment.
There are several starting positions and several goals for each starting position. In order to confront the robot with a variety of environments different obstacle configurations have been devised. The complete environment is shown on the left in the following figure. The numbers indicate the input, which depends on the distance to the obstacle. It is endowed with a sensory system and a neural net.