Abstract
In this paper we implement GAs that have one or more parameters that
are adjusted during the run. In particular we use an existing self-adaptive mutation
rate mechanism, propose a new mechanism for self-adaptive crossover rates, and
redesign an existing variable population size model. We compare the simple GA with
GAs featuring only one of the parameter adjusting mechanisms and with a GA that
applies all three mechanisms - and is therefore almost "parameterless". The
experimental results on a carefully designed test suite indicate the superiority of the
parameterless GA and give a hint on the power of adapting the population size.
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