Abstract
We outline the characteristics of the adaptive, distributed routing problem in communication networks and discuss the problem in relationship to recent research in distributed artificial intelligence and computational ecologies. We offer a brief overview of genetics-based machine learning applied to fuzzy control and present details of a Pittsburgh-style fuzzy classifier system which is used as a routing control agent. Experiments are described in which distributed fuzzy routing controllers are evolved in a small-scale and symmetrical, simulated network. The performance of evolved fuzzy controllers is compared to that of traditional controllers using the shortest-path routing algorithm. Possible ways forward in extending this approach to routing control in real networks are suggested.
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© 1995 Springer-Verlag Berlin Heidelberg
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Carse, B., Fogarty, T.C., Munro, A. (1995). Evolutionary learning in computational ecologies: An application to adaptive distributed routing in communication networks. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1995. Lecture Notes in Computer Science, vol 993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60469-3_29
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DOI: https://doi.org/10.1007/3-540-60469-3_29
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