Modulus genetic algorithm and its application to fuzzy system optimization
Sinn‐Cheng Lin · 1999
The conventional genetic algorithm encodes the searched parameters as binary strings. After applying the basic genetic operators such as reproduction, crossover and mutation, a decoding procedure is used to convert the binary strings to the original parameter space. As the result, such an encoding/decoding procedure leads to considerable numeric errors. This paper proposes a new algorithm called modulus genetic algorithm (MGA) that uses the modulus operation to resolve this problem. In the MGA, the encoding/decoding procedure is not necessary. It has the following advantages: 1) the evolution can be speeded up; 2) the numeric truncation error can be avoided; 3) the precision of solution can be increased. The proposed MGA is applied to resolve the key problem of fuzzy inference systems-rule acquisition. The fuzzy system with MGA as learning mechanism forms an "intelligent fuzzy system". Based on the proposed approach, the fuzzy rule base can be self-extracted and optimized.