Abstract
Graphs are powerful and versatile data structures, useful to represent complex and structured information of interest in various fields of science and engineering. We present a system, called EvoGeneS, based on an evolutionary approach, for generating undirected graphs whose number of nodes is not a priori known. The method is based on a special data structure, called multilist, which encodes undirected attributed relational graphs. Two novel crossover and mutation operators are defined in order to evolve such structure. The developed system has been tested on a wireless network configuration and the results compared with those obtained by a genetic programming based approach recently proposed in the literature.
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Cordella, L.P., De Stefano, C., Fontanella, F., Marcelli, A. (2005). EvoGeneS, a New Evolutionary Approach to Graph Generation. In: Raidl, G.R., Gottlieb, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2005. Lecture Notes in Computer Science, vol 3448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31996-2_5
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DOI: https://doi.org/10.1007/978-3-540-31996-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25337-2
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