Co-evolving robot controllers that generalize well

To evolve robot controllers that generalize well, we should evaluate the controllers using as many environment patterns or test cases as possible. On the other hand, to evolve the controllers faster, we should use as few environment patterns as possible in evaluation. It is difficult to know in advance what environment patterns can produce good controllers. To solve this problem, this paper studies co-evolution of the robot controllers and the environment patterns. To improve the effectiveness of co-evolution, we introduce fitness sharing in the population of environment patterns, and the inter-generation fitness in selecting the robot controllers. Simulation results show that the improved method can get much better robot controllers than standard co-evolutionary algorithm.