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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Lévy-Flight Genetic Programming: Towards a New Mutation Paradigm
verfasst von : Christian Darabos, Mario Giacobini, Ting Hu, Jason H. Moore
Erschienen in: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Verlag: Springer Berlin Heidelberg
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Lévy flights are a class of random walks inspired directly by observing animal foraging habits, in which the stride length is drawn from a power-law distribution. This implies that the vast majority of the strides will be short. However, on rare occasions, the stride are gigantic. We use this technique to self-adapt the mutation rate used in Linear Genetic Programming. We apply this original approach to three different classes of problems: Boolean regression, quadratic polynomial regression, and surface reconstruction. We find that in all cases, our method outperforms the generic, commonly used constant mutation rate of 1 over the size of the genotype. We compare different common values of the power-law exponent to the regular spectrum of constant values used habitually. We conclude that our novel method is a viable alternative to constant mutation rate, especially because it tends to reduce the number of parameters of genetic programing.