The NK model of fitness landscape considers N states which can interact with K other states. For example N is the total number of genes in a haploid genome and K is the number of other genes that each gene may interact with.
In this article Kaufman says:
The fully connected NK model yields a completely random fitness landscape. For K = N - 1, the fitness contribution of each site depends on all of the other sites in the sequence and therefore altering any site from one to the other value, 0-1, alters the fitness contribution of each site to a new random value. Thus the fitness of any 1-mutant neighboring sequence is completely random with respect to the initial sequence. The landscape is fully random. As was shown in Kauffman & Levin (1987), Weinberger (1988), and Macken & Pereison (1989), such random landscapes have very many local optima, on average, 2N/(N +1).
I did not read the references mentioned in the text but I read the related section in Kaufman's book: Origins of Order: Self-Organization and Selection in Evolution.
In the book he says that the fitness of a genotype can be ordered and given ranks; the probability that a genotype is fitter than its neighbours (local optima) is same as its probability of ranking higher than its N neighbours. So this probability:
and if each gene can have only two states lets say on or off then the expected number of optima with respect to one-mutants (i.e. the difference between a gene and its neighbour is just one mutation) would be:
What I don't understand is the role of K in this expression. This reason should be valid for any number of K (See the quote above). Even in the book this explanation is given in the case where K is maximum i.e. N-1. I can understand that K can affect the ruggedness of the landscape but can anyone explain how K affects the number of optima? (or are they just two separate statements written together, which is confusing me)