Abstract
The parameter-less genetic algorithm was introduced a couple of years ago as a way
to simplify genetic algorithm operation by incorporating knowledge of parameter
selection and population sizing theory in the genetic algorithm itself. This paper shows
how that technique can be used in practice by applying it to a network expansion
problem. The existence of the parameter-less genetic algorithm stresses the fact that
some problems need more processing power than others. Such observation leads to the
development of a problem difficulty measure which is also introduced in this paper. The
measure can be useful for comparing the difficulty of real-world problems.
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