Abstract
In this paper we combine the concept of evolutionary hill-climbing search with the systematic search concept of arc revision to form a hybrid system that quickly finds solutions to static and dynamic Fuzzy Constraint Truth Optimization Problems (FCTOPs). We compare the static and dynamic methods for solving FCTOPs using a test suite of an additional 250 randomly generated FCTOPs. In the static method, all the constraints of a FCTOP are known by the hybrid system at runtime. In the dynamic method, only half of the constraints of a FCTOP are known at run-time. Each time our hybrid system discovers a solution that satisfies all of the constraints of the the current network, one additional constraint is added. Results show that our algorithm performs exceptionally well in the presence of dynamic (incremental) constraints.
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© 1997 Springer-Verlag Berlin Heidelberg
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Dozier, G., Bowen, J., Homaifar, A., Esterline, A. (1997). Solving static and dynamic fuzzy constraint networks using evolutionary hill-climbing. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhart, R. (eds) Evolutionary Programming VI. EP 1997. Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014811
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DOI: https://doi.org/10.1007/BFb0014811
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