Extending GENET to solve fuzzy constraint satisfaction problems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Constraint Representation for Propagation
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Removing Node and Edge Overlapping in Graph Layouts by A Modified EGENET Solver
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
An efficient algorithm for online square detection
Theoretical Computer Science - Computing and combinatorics
Integer optimization by local search: a domain-independent approach
Integer optimization by local search: a domain-independent approach
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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, GENET is deficient 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 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.