Symbiotic Evolution Genetic Algorithms for Reinforcement Fuzzy Systems Design

The advent of fuzzy logic controllers has inspired the allocation of new resources for the possible realization of more efficient methods of control. In comparison with traditional controller design methods requiring mathematical models of the plants, one key advantage of fuzzy controller design lies in its model-free approach. Conventionally, the selection of fuzzy if-then rules often relies heavily upon the substantial amounts of heuristic observation to express the strategy's proper knowledge. It is very difficult for human experts to examine all the input-output data from a complex system, and then to design a number of proper rules for the fuzzy logic controllers. Many design approaches for automatic fuzzy rules generation have been developed in an effort to tackle this problem (Lin & Lee, 1996). The neural learning method is one of them. In (Miller et al., 1990), several neural learning methods including supervised and reinforcement based control configurations are studied. For many control problems, the training data are usually difficult and expensive, if not impossible, to obtain. Besides, many control problems require selecting control actions whose consequences emerge over uncertain periods for which training data are not readily available. In reinforcement learning, agents learn from signals that provide some measure of performance which may be delivered after a sequence of decisions being made. Hence, when the above mentioned control problems occur, reinforcement learning is more appropriate than supervised learning.