Berlanga de Jesús, AntonioSanchis de Miguel, María AraceliIsasi, PedroMolina López, José Manuel2009-04-152009-04-152002-02Journal of Intelligent and Robotic Systems, 2002, vol. 33, n. 2, p. 139-1661573-0409 (Online)https://hdl.handle.net/10016/3981In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.application/pdfeng© SpringerRobot navigation problemGeneralized behaviorCompetitive coevolutionLearning examples-basedEvolutionary strategiesresearch articleInformática10.1023/A:1014643811186open access1392166Journal of Intelligent and Robotic Systems33