In this paper we investigate the properties of CEAs with populations structured as Watts-Strogatz small-world graphs and Albert-Barabasi 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.
Effects of Scale-Free and Small-World Topologies on Binary Coded Self-Adaptive CEA
GIACOBINI, Mario Dante Lucio;
2006-01-01
Abstract
In this paper we investigate the properties of CEAs with populations structured as Watts-Strogatz small-world graphs and Albert-Barabasi 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.File in questo prodotto:
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