AUTOMATICALLY INTEGRATING MULTIPLE RULE SETS IN A DISTRIBUTED-KNOWLEDGE ENVIRONMENT

In this paper, an actual knowledge application is made by means of evo lution paradigms in terms of knowledge acquisition. rin automatic know ledge integration approach in a distributed-knowledge environment is t hus proposed to integrate multiple rule sets into a single effective r ule set. The proposed approach consists of two phases: knowledge encod ing and knowledge integration. In the encoding phase, each knowledge i nput is translated and expressed as a rule set, then encoded as a bit string. The combined bit strings from multiple knowledge inputs form a n initial knowledge population, which is then ready for integration. I n the knowledge integration phase, a genetic search technique generate s an optimal or nearly optimal rule set from these initial knowledge-i nput strings. Finally, experimental results from diagnosis of brain tu mors show that the rule set derived by the proposed approach is much m ore accurate than each initial rule set.