  Slow self-avoiding adaptive walks by an infinite radius search algorithm
(Limax) are analyzed as themselves, and as the network they form. The study is
conducted on several NK problems and two HIFF problems. We find that
examination of such "slacker" walks and networks can indicate relative search
difficulty within a family of problems, help identify potential local optima,
and detect presence of structure in fitness landscapes. Hierarchical walks are
used to differentiate rugged landscapes which are hierarchical (e.g. HIFF) from
those which are anarchic (e.g. NK). The notion of node viscidity as a measure
of local optimum potential is introduced and found quite successful although
more work needs to be done to improve its accuracy on problems with larger K.
