Extending GENET for Non-Binary Constraint Satisfaction Problems
نویسندگان
چکیده
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.
منابع مشابه
Extending GENET with lazy arc consistency
Constraint satisfaction problems (CSP's) naturally occur in a number of important industrial applications, such as planning, scheduling and resource allocation. GENET is a neural network simulator to solve binary constraint satisfaction problems. GENET uses a convergence procedure based on a relaxed form of local consistency to nd assignments which are locally minimal in terms of constraint vio...
متن کاملAnd Tam : Extending Genet with Lazy Arc Consistency 101
| Many important applications, such as graph coloring, scheduling and production planning, can be solved by GENET, a local search method which is used to solve binary constraint satisfaction problems (CSPs). Where complete search methods are typically augmented with consistency methods to reduce the search, local search methods are not. We propose a consistency technique, lazy arc consistency ,...
متن کاملGENET: A Connectionist Architecture for Solving Constraint Satisfaction Problems by Iterative Improvement
New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the million queens. However, on highly constrained problems it is possible for these methods to get caught in local minima. In this paper we present genet, a connectionist architecture for solving binary and general constraint...
متن کاملGENET: A Connectionist Architecture for Solving Constraint Sat isfact ion terative rovement*
New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the million queens. However, on highly constrained problems it is possible for these methods to get caught in local minima. In this paper we present GENET, a connectionist architecture for solving binary and general constraint...
متن کاملExtending GENET for non-binary CSP's
GENET has been shown to be efficient and effective on certain hard or large constraint satasfaction 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. In this paper, we present E-GENET, an extended GENET. E-GENET features a convergence and learning procedur...
متن کامل