Assessing the performance of cultural algorithms for semantic network re-engineering

Evolutionary computation has been successfully applied in a variety of problem domains and applications. The authors discuss the use of a specific form of evolutionary computation known as cultural algorithms to improve the efficiency of the subsumption algorithm in semantic networks. Subsumption is the process that determines if one node in the network is a child of another node. As such, it is utilized as part of the node classification algorithm within semantic network based applications. One method of improving subsumption efficiency is to reduce the number of attributes that need to be compared for every node without impacting the results. We demonstrate that a cultural algorithm approach can be used to identify these defining attributes that are most significant for node retrieval. These results can then be utilized within an existing vehicle assembly process planning application that utilizes a semantic network based knowledge base to improve the performance and reduce complexity of the network