  When a considerable number of mutations have no effects on fitness values,
the fitness landscape is said neutral. In order to study the interplay between
neutrality, which exists in many real-world applications, and performances of
metaheuristics, it is useful to design landscapes which make it possible to
tune precisely neutral degree distribution. Even though many neutral landscape
models have already been designed, none of them are general enough to create
landscapes with specific neutral degree distributions. We propose three steps
to design such landscapes: first using an algorithm we construct a landscape
whose distribution roughly fits the target one, then we use a simulated
annealing heuristic to bring closer the two distributions and finally we affect
fitness values to each neutral network. Then using this new family of fitness
landscapes we are able to highlight the interplay between deceptiveness and
neutrality.
