Abstract
Although there are some real world applications where
the use of variable length representation (VLR) in
Evolutionary Algorithm is natural and suitable, an
academic framework is lacking for such representations.
In this work we propose a family of tunable fitness
landscapes based on VLR of genotypes. The fitness
landscapes we propose possess a tunable degree of both
neutrality and epistasis; they are inspired, on the one
hand by the Royal Road fitness landscapes, and the
other hand by the NK fitness landscapes. So these
landscapes offer a scale of continuity from Royal Road
functions, with neutrality and no epistasis, to
landscapes with a large amount of epistasis and no
redundancy. To gain insight into these fitness
landscapes, we first use standard tools such as
adaptive walks and correlation length. Second, we
evaluate the performances of evolutionary algorithms on
these landscapes for various values of the neutral and
the epistatic parameters; the results allow us to
correlate the performances with the expected degrees of
neutrality and epistasis.
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