Abstract
Evolutionary computation has been successfully applied in a variety of problem domains and applications. In this paper we describe the use of a specific form of evolutionary computation known as cultural algorithms to solve the problem of semantic network reformulation. The semantic network knowledge base is based on the KL-ONE knowledge representation model and contains all the relevant information about the automobile manufacturing process planning system at Ford Motor Company. The complexity of the application along with the frequent changes necessitated by the dynamic nature of the automobile industry has led to frequent modifications to the knowledge base. The explosive growth of the knowledge base has also increased retrieval time for the users. In this paper we suggest that a cultural algorithm approach can be used to identify the attributes that are most significant for node retrieval and describe how to utilize this knowledge to create a more efficient and less complex semantic network.
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© 1998 Springer-Verlag Berlin Heidelberg
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Rychtyckyj, N., Reynolds, R.G. (1998). Learning to re-engineer semantic networks using cultural algorithms. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040771
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DOI: https://doi.org/10.1007/BFb0040771
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