Extending GENET for non-binary CSP's

GENET has been shown to be efficient and effective 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 deficient in handling non binary constraints in general. 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 disjunctive constraints to non linear constraints to symbolic constraints. We have implemented an efficient prototype of E-GENET for single processor machines. Benchmarking results confirms the efficiency and flexibility of E-GENET. Our implementation also compares well against CHIP, PROCLANN, and GENET.

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