Generalization capabilities of co‐evolution in learning robot behavior

摘要

Abstract In this article, a co‐evolutive method is used to evolve neural controllers for general obstacle‐avoidance of a Braitenberg vehicle. During a first evolutionary process, Evolution Strategies were applied to generate neural controllers; the generality of the obtained behaviors was quite poor. During a second evolutionary process, a new co‐evolutive method, called Uniform Co‐evolution, is introduced to co‐evolve both the controllers and the environment. A comparison of both methods shows that the co‐evolutive approach improves the generality of controllers. © 2002 Wiley Periodicals, Inc.