6 research outputs found
E-GENET: a stochastic constraint solver.
by Won, Hon Wing Stephen.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 95-101).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Constraint Satisfaction Problem --- p.1Chapter 1.2 --- CSP Solving Techniques --- p.2Chapter 1.3 --- Motivation of the Dissertation --- p.4Chapter 1.4 --- Overview of the Dissertation --- p.6Chapter 2 --- Related Work --- p.8Chapter 2.1 --- Heuristic Repair Method --- p.8Chapter 2.2 --- GSAT --- p.8Chapter 2.3 --- GENET --- p.9Chapter 2.4 --- Simulated Annealing --- p.9Chapter 2.5 --- Genetic Algorithms --- p.10Chapter 3 --- Overview of GENET --- p.11Chapter 3.1 --- Network Architecture --- p.11Chapter 3.2 --- Convergence Procedure --- p.12Chapter 3.3 --- The illegal and atmost Constraints --- p.13Chapter 3.3.1 --- The illegal Constraint --- p.14Chapter 3.3.2 --- The atmost Constraint --- p.14Chapter 3.4 --- General Non-Binary Constraints --- p.15Chapter 3.4.1 --- Constraint Transformation --- p.15Chapter 3.4.2 --- Using the illegal Constraints --- p.17Chapter 3.4.3 --- Problem Transformation --- p.17Chapter 4 --- An Extended GENET --- p.20Chapter 4.1 --- Network Architecture --- p.20Chapter 4.2 --- Convergence Procedure --- p.22Chapter 4.3 --- E-GENET as a Generalization of GENET --- p.24Chapter 4.3.1 --- Constraints --- p.30Chapter 4.3.2 --- Network Architecture --- p.31Chapter 4.4 --- Properties of E-GENET --- p.32Chapter 4.4.1 --- Incompleteness of E-GENET --- p.33Chapter 4.4.2 --- Making E-GENET Complete --- p.36Chapter 4.5 --- Storage Scheme --- p.38Chapter 4.5.1 --- The illegal and atmost Constraint --- p.39Chapter 4.5.2 --- The Disequality Constraint --- p.39Chapter 4.5.3 --- General Constraints --- p.40Chapter 4.6 --- Benchmarking Results --- p.40Chapter 4.6.1 --- The Graph-Coloring Problem --- p.41Chapter 4.6.2 --- The N-queens Problem --- p.42Chapter 4.6.3 --- The Car-Sequencing Problem --- p.43Chapter 4.6.4 --- The Cryptarithmetic Problem --- p.44Chapter 4.6.5 --- The Hamiltonian Path Problem --- p.45Chapter 5 --- Optimizations to E-GENET --- p.47Chapter 5.1 --- Inadequacies of E-GENET --- p.47Chapter 5.1.1 --- Cumbrous Constraint Node --- p.48Chapter 5.1.2 --- Inefficiency of the min-conflicts heuristic --- p.48Chapter 5.2 --- Optimizations --- p.50Chapter 5.2.1 --- Intermediate Node --- p.50Chapter 5.2.2 --- New Assignment Scheme of Initial Penalty Values --- p.55Chapter 5.2.3 --- Concept of Contribution --- p.57Chapter 5.2.4 --- Learning Heuristic --- p.62Chapter 6 --- A Comprehensive Constraint Library --- p.63Chapter 6.1 --- Elementary Constraints --- p.64Chapter 6.1.1 --- Linear Arithmetic Constraints --- p.64Chapter 6.1.2 --- The atmost Constraint --- p.66Chapter 6.1.3 --- Disjunctive Constraints --- p.69Chapter 6.2 --- Global Constraints --- p.71Chapter 6.2.1 --- The cumulative Constraint --- p.72Chapter 6.2.2 --- The among Constraint --- p.77Chapter 6.2.3 --- The diffn Constraint --- p.82Chapter 6.2.4 --- The cycle Constraint --- p.85Chapter 7 --- Conclusion --- p.89Chapter 7.1 --- Contributions --- p.89Chapter 7.2 --- Discussions --- p.90Chapter 7.3 --- Future Work --- p.94Bibliography --- p.9
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Herausforderung im Gebiet der Algorithmen dar. Sie treten auf, wenn eine große
Anzahl diskreter organisatorischer Entscheidungen unter Berücksichtigung von
Constraints und Optimierungskriterien zu treffen sind. Diese Arbeit beschreibt und untersucht neue, domänenunabhängige Strategien der lokalen Suche zur ganzzahligen linearen Optimierung. Wir beschreiben WSAT(OIP), eine Strategie "ganzzahliger lokaler Suche\u27;, die auf einer algebraischen
Problemrepräsentation operiert. WSAT(OIP) verallgemeinert Walksat, eine
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Towards a More Efficient Stochastic Constraint Solver
. E-GENET shows certain success on extending GENET for non-binary CSP's. However, the generic constraint representation scheme of E-GENET induces the problem of storing too many penalty values in constraint nodes and the min-conflicts heuristic is not efficient enough on some problems. To overcome these two weaknesses and further improve the performance, we propose several modifications. All of them together can boost the efficiency of E-GENET without resorting to modifying the underlying network model or the convergence procedure in an ad hoc manner. The performance of modified E-GENET also compares well against that of CHIP. 1 Introduction Many problems in artificial intelligence and computer science in general can be formulated as constraint satisfaction problems (CSP's). Efficient algorithms for solving CSP's are thus very useful. In 1992, Minton et al. published a paper on a new approach for solving CSP's. The approach is known as heuristic repair method or iterative repair metho..