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29
Genet: A connectionist architecture for solving constraint satisfaction problems by iterative improvement
 In Proceedings of AAAI'94
, 1994
"... 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 th ..."
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Cited by 94 (20 self)
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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 satisfaction problems by iterative improvement. genet incorporates a learning strategy to escape from local minima. Although genet has been designed to be implemented on vlsi hardware, we present empirical evidence to show that even when simulated on a single processor genet can outperform existing iterative improvement techniques on hard instances of certain constraint satisfaction problems.
Edward,“Guided Local Search
, 1995
"... Abstract Combinatorial explosion problem is a well known phenomenon that prevents complete algorithms from solving many reallife combinatorial optimization problems. In many situations, heuristic search methods are needed. This chapter describes the principles of Guided Local Search (GLS) and Fast ..."
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Cited by 56 (5 self)
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Abstract Combinatorial explosion problem is a well known phenomenon that prevents complete algorithms from solving many reallife combinatorial optimization problems. In many situations, heuristic search methods are needed. This chapter describes the principles of Guided Local Search (GLS) and Fast Local Search (FLS) and surveys their applications. GLS is a penaltybased metaheuristic algorithm that sits on top of other local search algorithms, with the aim to improve their efficiency and robustness. FLS is a way of reducing the size of the neighbourhood to improve the efficiency of local search. The chapter also provides guidance for implementing and using GLS and FLS. Four problems, representative of general application categories, are examined with detailed information provided on how to build a GLSbased method in each case.
Fast Local Search and Guided Local Search and Their Application to British Telecom's Workforce Scheduling Problem
 Operations Research Letters
, 1995
"... This paper reports a Fast Local Search (FLS) algorithm which helps to improve the efficiency of hill climbing and a Guided Local Search (GLS) Algorithm which is developed to help local search to escape local optima and distribute search effort. To illustrate how these algorithms work, this paper des ..."
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Cited by 41 (20 self)
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This paper reports a Fast Local Search (FLS) algorithm which helps to improve the efficiency of hill climbing and a Guided Local Search (GLS) Algorithm which is developed to help local search to escape local optima and distribute search effort. To illustrate how these algorithms work, this paper describes their application to British Telecom's workforce scheduling problem, which is a hard real life problem. The effectiveness of FLS and GLS are demonstrated by the fact that they both outperform all the methods applied to this problem so far, which include simulated annealing, genetic algorithms and constraint logic programming. I. Introduction Due to their combinatorial explosion nature, many real life constraint optimization problems are hard to solve using complete methods such as branch & bound [17, 14, 21, 23]. One way to contain the combinatorial explosion problem is to sacrifice completeness. Some of the best known methods which use this strategy are local search methods, the ba...
Constraint Programming: In Pursuit of the Holy Grail
 in Proceedings of WDS99 (invited lecture
, 1999
"... : Constraint programming (CP) is an emergent software technology for declarative description and effective solving of large, particularly combinatorial, problems especially in areas of planning and scheduling. It represents the most exciting developments in programming languages of the last decade a ..."
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Cited by 39 (0 self)
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: Constraint programming (CP) is an emergent software technology for declarative description and effective solving of large, particularly combinatorial, problems especially in areas of planning and scheduling. It represents the most exciting developments in programming languages of the last decade and, not surprisingly, it has recently been identified by the ACM (Association for Computing Machinery) as one of the strategic directions in computer research. Not only it is based on a strong theoretical foundation but it is attracting widespread commercial interest as well, in particular, in areas of modelling heterogeneous optimisation and satisfaction problems. In the paper, we give a survey of constraint programming technology and its applications starting from the history context and interdisciplinary nature of CP. The central part of the paper is dedicated to the description of main constraint satisfaction techniques and industrial applications. We conclude with the overview of limit...
Guided local search for solving SAT and weighted MAXSAT problems
 Journal of Automated Reasoning
, 2000
"... Abstract. In this paper, we show how Guided Local Search (GLS) can be applied to the SAT problem and show how the resulting algorithm can be naturally extended to solve the weighted MAXSAT problem. GLS is a general, penaltybased metaheuristic, which sits on top of local search algorithms to help g ..."
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Cited by 33 (7 self)
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Abstract. In this paper, we show how Guided Local Search (GLS) can be applied to the SAT problem and show how the resulting algorithm can be naturally extended to solve the weighted MAXSAT problem. GLS is a general, penaltybased metaheuristic, which sits on top of local search algorithms to help guide them out of local minima. GLS has been shown to be successful in solving a number of practical real life problems, such as the travelling salesman problem, BT's workforce scheduling problem, the radio link frequency assignment problem and the vehicle routing problem. We present empirical results of applying GLS to instances of the SAT problem from the DIMACS archive and also a small set of weighted MAXSAT problem instances and compare them against the results of other local search algorithms for the SAT problem. Keywords: SAT problem, Local Search, Metaheuristics, Optimisation 1.
Partial constraint satisfaction problems and guided local search
 Proc., Practical Application of Constraint Technology (PACT'96
, 1996
"... A largely unexplored aspect of Constraint Satisfaction Problem (CSP) is that of overconstrained instances for which no solution exists that satisfies all the constraints. In these problems, mentioned in the literature as Partial Constraint Satisfaction Problems (PCSPs), we are often looking for sol ..."
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Cited by 31 (12 self)
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A largely unexplored aspect of Constraint Satisfaction Problem (CSP) is that of overconstrained instances for which no solution exists that satisfies all the constraints. In these problems, mentioned in the literature as Partial Constraint Satisfaction Problems (PCSPs), we are often looking for solutions which violate the minimum number of constraints. In more realistic settings, constraints violations incur different costs and solutions are sought that minimize the total cost from constraint violations and possibly other criteria. Problems in this category present enormous difficulty to complete search algorithms. In practical terms, complete search has more or less to resemble the traditional Branch and Bound taking no advantage of the efficient pruning techniques recently developed for CSPs. In this report, we examine how the stochastic search method of Guided Local Search (GLS) can be applied to these problems. The effectiveness of the method is demonstrated on instances of the Radio Link Frequency Assignment Problem (RLFAP), which is a realworld Partial CSP.
Connectionist Inference Systems
, 1991
"... This paper presents a survey of connectionist inference systems. ..."
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Cited by 25 (6 self)
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This paper presents a survey of connectionist inference systems.
Automated Inferencing and Connectionist Models
, 1993
"... this paper. This set may be empty in which case the subgoal is unsolvable. However, if we admit arbitrary logic programs which include equality, then a set of minimal solutions may not exist (see [41]). ..."
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Cited by 19 (5 self)
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this paper. This set may be empty in which case the subgoal is unsolvable. However, if we admit arbitrary logic programs which include equality, then a set of minimal solutions may not exist (see [41]).
Extending GENET with lazy arc consistency
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS
, 1996
"... 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 ..."
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Cited by 11 (4 self)
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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 find assignments which are locally minimal in terms of constraint violation. It uses heuristic learning to escape local minima which do not represent solutions. We describe a lazy arc consistency technique which is suitable for integration into the convergence procedure of GENET. We compare the efficiency of the GENET using lazy arc consistency against GENET, both alone and using a full arc consistency preprocessing step, on a number of hard or large instances of binary CSP's. GENET with lazy arc consistency betters the original GENET on instances of binary CSP's which are not arc consistent in their original formulation, and does not suffer the overhead of full...
The Tunneling Algorithm for Partial CSPs and Combinatorial Optimization Problems
, 1994
"... Constraint satisfaction is the core of a large number of problems, notably scheduling. Because of their potential for containing the combinatorial explosion problem in constraint satisfaction, local search methods have received a lot of attention in the last few years. The problem with these methods ..."
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Cited by 9 (2 self)
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Constraint satisfaction is the core of a large number of problems, notably scheduling. Because of their potential for containing the combinatorial explosion problem in constraint satisfaction, local search methods have received a lot of attention in the last few years. The problem with these methods is that they can be trapped in local minima. GENET is a connectionist approach to constraint satisfaction. It escapes local minima by means of a weight adjustment scheme, which has been demonstrated to be highly effective. The tunneling algorithm described in this paper is an extension of GENET for optimization. The main idea is to introduce modifications to the function which is to be optimized by the network (this function mirrors the objective function which is specified in the problem). We demonstrate the outstanding performance of this algorithm on constraint satisfaction problems, constraint satisfaction optimization problems, partial constraint satisfaction problems, radio frequency ...