Extending GENET for Non-Binary Constraint Satisfaction Problems

GENET has been shown to be eecient and eeective on certain hard or large constraint satisfaction problems. Although GENET has been enhanced to handle also the atmost and illegal constraints in addition to binary constraints, it is deecient in handling non-binary constraints in general. In this paper, we present E-GENET, an extended GENET. E-GENET features a convergence and learning procedure similar to that of GENET and a generic representation scheme for general constraints, which range from dis-junctive constraints to non-linear constraints to symbolic constraints. We have implemented an eecient prototype of E-GENET for single-processor machines. Benchmarking results connrms the eeciency and ex-ibility of E-GENET. Our implementation also compares well against CHIP, PROCLANN, and GENET.