  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.
