Abstract
Evolutionary multiobjective optimization (EMO) algorithms have attracted much research interest in recent years. In evolutionary robotics (ER), several papers have been published where EMO algorithms have been applied to design multiobjective behavior of autonomous robots. However, these are for either specific control tasks or controllers. Characteristics of EMO algorithms on design of a more popular controller for simple robot control tasks need to be investigated for fully understanding them in ER. In this work, a multiobjective genetic algorithm was applied to the design of a neural controller for multiobjective behavior of a mobile robot in a looping maze problem, which is a popular test problem for ER. Distribution of non-dominated solutions in the objective function space were obtained from a number of trials in the problem in order to investigate how preferred solutions are distributed in them.
