Abstract
The network design problem discussed in this paper deals with optimising network parameters that characterise the following topologies: ring, chordal ring, torus and hypercube. These topologies have known, advantageous characteristics that may be useful in a final solution. By devising a system that can measure the extent to which an arbitrary mesh approaches these topologies multi-objective genetic algorithms that include topology as a dimension can be developed. Multi-objective genetic algorithms allow the designer to choose the ’ideal’ design from a pareto-optimal surface. This paper describes a method by which such a measure can be obtained for a topology from a set of network parameters namely: minimum hop count, node eccentricity, node degree and the number of links. In order to prove that these measures are effective in the context of a genetic algorithm, test results are given for applying these measures as part of a fitness function for evolving the specified topologies from an ’arbitrary’ mesh network. The results obtained show that the measures used are suitable for measuring the extent to which an arbitrary mesh matches a known topology, within a fitness function. As a consequence the designer can be guaranteed a range of acceptable but different choices.
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© 1999 Springer-Verlag Berlin Heidelberg
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Webb, A., Turton, B., Brown, J. (1999). A Genetic Algorithm for Designing Networks with Desirable Topological Properties. In: Poli, R., Voigt, HM., Cagnoni, S., Corne, D., Smith, G.D., Fogarty, T.C. (eds) Evolutionary Image Analysis, Signal Processing and Telecommunications. EvoWorkshops 1999. Lecture Notes in Computer Science, vol 1596. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704703_14
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DOI: https://doi.org/10.1007/10704703_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65837-5
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