Correlation Heuristics for Constraint Programming
This work addresses the need for effective general-purpose search strategies in Constraint Programming, offering incremental improvements through novel heuristics.
The paper tackled the problem of improving search strategies in Constraint Programming by introducing variable correlations to guide search, resulting in correlation heuristics that are competitive with existing methods and fastest on many benchmarks.
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.