Correlation Heuristics for Constraint Programming
  Effective general-purpose search strategies are an important component in
Constraint Programming. We introduce a new idea, namely, using correlations
between variables to guide search. Variable correlations are measured and
maintained by using domain changes during constraint propagation. We propose
two variable heuristics based on the correlation matrix, crbs-sum and crbs-max.
We evaluate our correlation heuristics with well known heuristics, namely,
dom/wdeg, impact-based search and activity-based search. Experiments on a large
set of benchmarks show that our correlation heuristics are competitive with the
other heuristics, and can be the fastest on many series.
