A genetic fuzzy-knowledge integration framework

We propose a genetic fuzzy-knowledge integration framework that could effectively integrate multiple fuzzy rule-sets and their membership function sets simultaneously. The proposed approach consists of two phases: fuzzy-knowledge encoding and fuzzy-knowledge integration. In the encoding phase, each fuzzy rule set associated with its membership functions is first encoded as a string. The combined strings thus form an initial knowledge population which is then ready for integration. In the knowledge integration phase, a genetic algorithm is used to generate an optimal or nearly optimal set of fuzzy rules and membership functions from the initial knowledge population. Finally, the prediction of sugar-cane breeding was used to show the performance of the proposed knowledge-integration approach. Results show that the resulting fuzzy knowledge base using our approach performs better than each individual knowledge base