Skip to Main Content
One of the main advantages of fuzzy systems is their ability to design comprehensible models of real-world systems, thanks to the use of a fuzzy rule structure easily interpretable by human beings. This is especially useful for the design of fuzzy logic controllers, where the knowledge base can be extracted from expert knowledge. Even more, the availability of a readable structure allows the human expert to customize the fuzzy controller to different environments by manually tuning its components. Nevertheless, this tuning task is usually a time-consuming procedure when done manually, especially when several measures are considered to evaluate the controller performance, and thus the interest in the design of automatic tuning procedures for fuzzy systems has increased along the last few years. In this paper, we tackle the tuning of the fuzzy membership functions of a fuzzy visual system for autonomous robots. This fuzzy visual system is based on a hierarchical structure of three different fuzzy classifiers, whose combined action allows the robot to detect the presence of doors in the images captured by its camera. Although the global knowledge represented in the fuzzy system knowledge base makes it perform properly in the door detection task, its adaptation to the specific conditions of the environment where the robot is operating can significantly improve the classification accuracy. However, the tuning procedure is complex as two different performance indexes are involved in the optimization process (true positive and false positive detections), thus becoming a multiobjective problem. Hence, in order to automatically put the fuzzy system tuning into effect, different single and multiobjective evolutionary algorithms are considered to optimize the two criteria, and their behavior in problem solving is compared.