  We study the nature of fitness landscapes of 'quenched' Kauffman's Boolean
model with a scale-free network. We have numerically calculated the rugged
fitness landscapes, the distributions, its tails, and the correlation between
the fitness of local optima and their Hamming distance from the highest optimum
found, respectively. We have found that (a) there is an interesting difference
between the random and the scale-free networks such that the statistics of the
rugged fitness landscapes is Gaussian for the random network while it is
non-Gaussian with a tail for the scale-free network; (b) as the average degree
$<k>$ increases, there is a phase transition at the critical value of $<k > = <
k >_{c} = 2$, below which there is a global order and above which the order
goes away.
