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Non-universal Suffrage Selection Operators Favor Population Diversity in Genetic Algorithms
Federico Divina, Maarten Keijzer, and Elena Marchiori
Department of Computer Science
Vrije Universiteit
De Boelelaan 1081a
1081 HV Amsterdam, The Netherlands
{divina,mkeijzer,elena}@cs.vu.nl
Abstract. State-of-the-art concept learning systems based on genetic algorithms evolve a redundant population of individuals, where an individual is a partial solution that covers some instances of the learning set. In this context, it is fundamental that the population be diverse and that as many instances as possible be covered. The universal suffrage selection (US) operator is a powerful selection mechanism that addresses these two requirements. In this paper we compare experimentally the US operator with two variants, called Weighted US (WUS) and Exponentially Weighted US (EWUS), of this operator in the system ECL [1].
LNCS 2724, p. 1574 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003