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Refinement of knowledge representation parameters in fuzzy production rules by genetic algorithms

Focuses on using a genetic algorithm (GA) optimization technique to help knowledge engineers refine knowledge representation parameters (KRPs) which have been identified to be important and necessary to enhance the representation power of fuzzy production rules in the applications of fuzzy expert systems. These parameters include certainty factor, threshold value, fuzzy membership value, local and global weights. The gradient descent method in a multilayer perceptron neural network (NN) is replaced by a GA. The significance of such a refinement is that the refined or tuned parameters will enable the fuzzy expert system (FES) to draw more accurate and reasonable conclusions and reduce the chance of leading to a wrong goal. Furthermore, the time required to repeatedly consult with the domain experts will be reduced. The main concerns using a GA to solve this kind of problem are how to represent the problem by a GA and how to measure the fitness of each solution being considered. In the paper the representation method and fitness function are provided together with an experiment to demonstrate the GA approach to solve the refinement problem.