Abstract
In this paper we investigate the properties of CEAs with populations structured as Watts–Strogatz small-world graphs and Albert–Barabási scale-free graphs as problem solvers, using several standard discrete optimization problems as a benchmark. The EA variants employed include self-adaptation of mutation rates. Results are compared with the corresponding classical panmictic EA showing that topology together with self-adaptation drastically influences the search.
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Giacobini, M., Preuss, M., Tomassini, M. (2006). Effects of Scale-Free and Small-World Topologies on Binary Coded Self-adaptive CEA. In: Gottlieb, J., Raidl, G.R. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2006. Lecture Notes in Computer Science, vol 3906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11730095_8
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DOI: https://doi.org/10.1007/11730095_8
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