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.
2006
Evolutionary Computation in Combinatorial Optimization, EVOCOP2006
Budapest, Hungary
April 2006
Proceedings of Evolutionary Computation in Combinatorial Optimization, EVOCOP2006
Springer Verlag
3906
86
98
evolutionary computation; optimisation; irregular graph; structured population; scale free; small world
Giacobini, Mario Dante Lucio; Preuss, M; Tomassini, M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/5467
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