The traditional way of tackling discrete optimization problems is by using
local search on suitably defined cost or fitness landscapes. Such approaches
are however limited by the slowing down that occurs when local minima, that are
a feature of the typically rugged landscapes encountered, arrest the progress
of the search process. Another way of tackling optimization problems is by the
use of heuristic approximations to estimate a global cost minimum. Here we
present a combination of these two approaches by using cover-encoding maps
which map processes from a larger search space to subsets of the original
search space. The key idea is to construct cover-encoding maps with the help of
suitable heuristics that single out near-optimal solutions and result in
landscapes on the larger search space that no longer exhibit trapping local
minima. The processes that are typically employed involve some form of
coarse-graining, and we suggest here that they can be viewed as avatars of
renormalisation group transformations.

文章介绍了利用局部搜索和启发式逼近相结合的方法来解决离散优化问题，通过构建适当的启发式覆盖映射，实现从较大的搜索空间映射到原始搜索空间的子集，来避免困局性局部最小值的出现，并指出这种方法实际上可以被看做是重正化群变换的一种应用。