Evolutionary algorithms provide the ability to automatically design robot controllers, but their wider use is hampered by a number of problems, including the difficulty of obtaining complex behaviors. This paper proposes a biologically inspired indirect encoding method for developing neural networks that control autonomous agents. The model is divided into three stages, the first two stages determine the structure of the network – the positions of the neurons and the network connectivity, and the third stage, occurring during the lifetime of the agent, determines the strength of connections based on the network activity. The model was tested experimentally by simulating an agent in an artificial environment, and the results of these simulations show that the method successfully evolved agents, capable of distinguishing between several types of objects, collecting some while avoiding others, without the use of a complex fitness function.

No abstract