An Empirical Study on GAs "Without Parameters"

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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