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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Bäck, T., Eiben, A.E., van der Vaart, N.A.L. (2000). An Emperical Study on GAs “Without Parameters”. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_31
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DOI: https://doi.org/10.1007/3-540-45356-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41056-0
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