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Real Royal Road Functions for Constant Population Size
Tobias Storch and Ingo Wegener
Department of Computer Science
University of Dortmund
44221 Dortmund, Germany
tobias.storch@uni-dortmund.de
wegener@ls2.cs.uni-dortmund.de
Abstract.
Evolutionary and genetic algorithms (EAs and GAs) are quite
successful randomized function optimizers. This success is mainly
based on the interaction of different operators like selection,
mutation, and crossover. Since this interaction is still not well
understood, one is interested in the analysis of the single
operators. Jansen and Wegener (2001a) have described so-called
real royal road functions where simple steady-state GAs have a
polynomial expected optimization time while the success
probability of mutation-based EAs is exponentially small even
after an exponential number of steps. This success of the GA is
based on the crossover operator and a population whose
size is moderately increasing with the dimension of the search
space. Here new real royal road functions are presented where
crossover leads to a small optimization time, although the GA
works with the smallest possible population size - namely 
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LNCS 2724, p. 1406 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003