NK landscapes are random landscapes of tunable ruggedness, first introduced by and described in detail by Kauffman [40]. In brief, N denotes the total number of loci that define the landscape and K denotes the number of loci with which each locus interacts. The fitness of a genotype is determined as follows: For each locus we randomly choose a set of K other loci (K≤N−1). Then, for each locus, we generate a look-up table that assigns a random number drawn uniformly between 0 and 1 to each of the 2K+1 possible combinations of alleles at the K interacting loci and the focal locus. To obtain the fitness of a given genotype we multiply the fitness contributions from each locus, determined form the corresponding look-up tables, and scale the result so that the fittest genotype has fitness one. Landscapes with K = 0 (NK0), are single peaked and free of epistasis. In contrast, the landscapes with K = N−1 (NKN−1) are highly epistatic, multi-peaked random landscapes, where the fitness of each genotype is the product of N uniformly distributed random numbers.
http://ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000510#article1.body1.sec4.sec1.sec3.p1

Calculating fitness in the manner described results in landscapes that have very skewed distributions, and that are markedly different from the smooth and random landscapes as described.

K=0 is a smooth landscape, and K=5 is a random landscape, so why construct separate landscape (with different distributions) for those cases?

Are there benefits or caveats to using a highly skewed fitness distribution, and can it really be compared to a uniform distribution (e.g. as in Fig. 1 and 3)?