抄録
We propose a new mutation operator with an adaptively variable parameter selection probability in the genetic algorithm. The genetic algorithm is applied to optimize the fuzzy reasoning in this paper. Optimization of the fuzzy reasoning consists of two kinds of problem. These are a combinatorial and a numerical optimization problems which are found in optimizing configuration of fuzzy rules and optimizing shapes of membership functions of the fuzzy reasoning respectivelly. Although the genetic algorithm can be simultaneously applied to both two kinds of problem, the genetic algorithm has not applied to optimize the shape of the membership functions in many cases, because the genetic algorithm can not quickly obtain a solution which can be satisfied. To quickly obtain such a solution using the genetic algorithm, we have already proposed a variable bit-selection probability in the mutation operator. However, parameter selection probability, one of factor to configure the genetic algorithm, also greatly affects result of the optimization. To improve this difficulty, we propose the adaptively variable parameter selection probability which serves to a rapid convergence to the optimized solutions.
