  An algorithm (bliss) is proposed to speed up the construction of slow
adaptive walks. Slow adaptive walks are adaptive walks biased towards closer
points or smaller move steps. They were previously introduced to explore a
search space, e.g. to detect potential local optima or to assess the ruggedness
of a fitness landscape. To avoid the quadratic cost of computing Hamming
distance (HD) for all-pairs of strings in a set in order to find the set of
closest strings for each string, strings are sorted and clustered by bliss such
that similar strings are more likely to get paired off for HD computation. To
efficiently arrange the strings by similarity, bliss employs the idea of shared
non-overlapping position specific subsequences between strings which is
inspired by an alignment-free protein sequence comparison algorithm. Tests are
performed to evaluate the quality of b-walks, i.e. slow adaptive walks
constructed from the output of bliss, on enumerated search spaces. Finally,
b-walks are applied to explore larger search spaces with the help of
Wang-Landau sampling.
