Rank Based Evolution of Real Parameters on Noisy Fitness Functions: Evolving a Robot Neurocontroller

We present a Rank Based Evolutionary Algorithm for representations in the real numbers. We introduce a new Rank Based Selection operator and a new variation of a Rank Based Mutation that act in a representation using real numbers. The problem in which we tested the algorithm was to evolve a fixed topology feed forward artificial neural network that is used as a controller for a robot. In order to be successful, the robot must be able to use both proximity sensors and video input but there is some level of noise in them. The test results show how the proposed operators are suitable for this kind of problems where the fitness landscape is noisy and where little else is known about it.