A simulation program called Radiats is introduced which provides a simple simulated environment based on real-world Newtonian physics in which to evolve agent control architectures. Various experiments intended to improve controller performance and illustrate the advantages of morphogenetic models are then described. Finally, the viability of such techniques is critically discussed and conclusions are drawn. One of the tasks we focus our interest on is the one of having a simulated robot capable of bumping intelligently into certain targets, e.g. to send a ball to a desired location. This is similar to an augmented form of homing behaviour.
Artificial evolution is used to automatically design a neural network controller.
Some experiments with various tasks and settings show the potentials of artificial evolution used with neural networks for generating interesting behaviour. The agent-environment interaction that occurs directly after its conception plays a very large role in this. This is commonly known as the development phase. The final phenotype that an organism will possess, both physically and cognitively, is a product of this process. Some of the issues of artificial development are explored.