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Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
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Cited by 194 (14 self)
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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
QuickXPlain: Conflict Detection for Arbitrary Constraint Propagation Algorithms
, 2001
"... Existing conflict detection methods for CSP's such as [de Kleer, 1989; Ginsberg, 1993] cannot make use of powerful propagation which makes them unusable for complex realworld problems. On the other hand, powerful constraint propagation methods lack the ability to extract dependencies or confli ..."
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Cited by 74 (0 self)
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Existing conflict detection methods for CSP's such as [de Kleer, 1989; Ginsberg, 1993] cannot make use of powerful propagation which makes them unusable for complex realworld problems. On the other hand, powerful constraint propagation methods lack the ability to extract dependencies or conflicts, which makes them unusable for many advanced AI reasoning methods that require conflicts, as well as for interactive applications that require explanations. In this paper, we present a nonintrusive conflict detection algorithm called QUICKXPLAIN that tackles those problems. It can be applied to any propagation or inference algorithm as powerful as it may be. Our algorithm improves the efficiency of direct nonintrusive conflict detectors by recursively partitioning the problem into subproblems of half the size and by immediately skipping those subproblems that do not contain an element of the conflict. QUICKXPLAIN is used as explanation component of an advanced industrial constraintbased configuration tool.
The PaLM system: explanationbased constraint programming
 In Proceedings of TRICS: Techniques foR Implementing Constraint programming Systems, a postconference workshop of CP 2000
, 2000
"... Explanationbased constraint programming is a new way of solving constraint problems: it allows to propagate constraints of the problem, learning from failure and from the solver (thanks to recording explanations) and finally allows to get rid of backtrackbased complete searches by allowing more fr ..."
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Cited by 66 (13 self)
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Explanationbased constraint programming is a new way of solving constraint problems: it allows to propagate constraints of the problem, learning from failure and from the solver (thanks to recording explanations) and finally allows to get rid of backtrackbased complete searches by allowing more free moves in the search space (while remaining complete). This paper presents the PaLM system, an implementation of an explanationbased constraint programming system in CHOCO a constraint programming layer on top of CLAIRE.
Constraint Solving in Uncertain and Dynamic Environments: A Survey
 Constraints
, 2005
"... Abstract. This article follows a tutorial, given by the authors on dynamic constraint solving at CP 2003 [87]. It aims at offering an overview of the main approaches and techniques that have been proposed in the domain of constraint satisfaction to deal with uncertain and dynamic environments. Keywo ..."
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Cited by 26 (3 self)
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Abstract. This article follows a tutorial, given by the authors on dynamic constraint solving at CP 2003 [87]. It aims at offering an overview of the main approaches and techniques that have been proposed in the domain of constraint satisfaction to deal with uncertain and dynamic environments. Keywords: constraint satisfaction problem, uncertainty, change, stability, robustness, flexibility
A new approach to modeling and solving minimal perturbation problems
 In Recent Advances in Constraints
, 2004
"... Abstract. Formulation of many reallife problems evolves when the problem is being solved. For example, a change in the environment might appear after the initial problem specification and this change must be reflected in the solution. Such changes complicate usage of a traditionally static constrai ..."
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Cited by 16 (4 self)
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Abstract. Formulation of many reallife problems evolves when the problem is being solved. For example, a change in the environment might appear after the initial problem specification and this change must be reflected in the solution. Such changes complicate usage of a traditionally static constraint satisfaction technology that requires the problem to be fully specified before the solving process starts. In this paper, we propose a new formal description of changes in the problem formulation called a minimal perturbation problem. This description focuses on the modification of the solution after a change in the problem specification. We also describe a new branchandbound like algorithm for solving such type of problems.
Identifying and Exploiting Problem Structures Using Explanationbased Constraint Programming
 Constraints
"... Abstract. Identifying structures in a given combinatorial problem is often a key step for designing efficient search heuristics or for understanding the inherent complexity of the problem. Several Operations Research approaches apply decomposition or relaxation strategies upon such a structure ident ..."
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Cited by 15 (2 self)
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Abstract. Identifying structures in a given combinatorial problem is often a key step for designing efficient search heuristics or for understanding the inherent complexity of the problem. Several Operations Research approaches apply decomposition or relaxation strategies upon such a structure identified within a given problem. The next step is to design algorithms that adaptively integrate that kind of information during search. We claim in this paper, inspired by previous work on impactbased search strategies for constraint programming, that using an explanationbased constraint solver may lead to collect invaluable information on the intimate dynamically revealed and static structures of a problem instance. Moreover, we discuss how dedicated OR solving strategies (such as Benders decomposition) could be adapted to constraint programming when specific relationships between variables are exhibited. 1.
Solutionguided multipoint constructive search for job shop scheduling
 Journal of Artificial Intelligence Research
"... SolutionGuided MultiPoint Constructive Search (SGMPCS) is a novel constructive search technique that performs a series of resourcelimited tree searches where each search begins either from an empty solution (as in randomized restart) or from a solution that has been encountered during the search. ..."
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Cited by 14 (2 self)
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SolutionGuided MultiPoint Constructive Search (SGMPCS) is a novel constructive search technique that performs a series of resourcelimited tree searches where each search begins either from an empty solution (as in randomized restart) or from a solution that has been encountered during the search. A small number of these “elite ” solutions is maintained during the search. We introduce the technique and perform three sets of experiments on the job shop scheduling problem. First, a systematic, fully crossed study of SGMPCS is carried out to evaluate the performance impact of various parameter settings. Second, we inquire into the diversity of the elite solution set, showing, contrary to expectations, that a less diverse set leads to stronger performance. Finally, we compare the best parameter setting of SGMPCS from the first two experiments to chronological backtracking, limited discrepancy search, randomized restart, and a sophisticated tabu search algorithm on a set of wellknown benchmark problems. Results demonstrate that SGMPCS is significantly better than the other constructive techniques tested, though lags behind the tabu search. 1.
Using explanations for designpatterns identification
 proceedings of the 1 st IJCAI workshop on Modeling and Solving Problems with Constraints
, 2001
"... Design patterns describe microarchitectures that solve recurrent architectural problems in objectoriented programming languages. It is important to identify these microarchitectures during the maintenance of objectoriented programs. But these microarchitectures often appear distorted in the sour ..."
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Cited by 14 (9 self)
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Design patterns describe microarchitectures that solve recurrent architectural problems in objectoriented programming languages. It is important to identify these microarchitectures during the maintenance of objectoriented programs. But these microarchitectures often appear distorted in the source code. We present an application of explanationbased constraint programming for identifying these distorted microarchitectures. 1
Local Search and Backtracking vs NonSystematic Backtracking
 In AAAI 2001 Fall Symposium on Using Uncertainty within Computation
, 2001
"... This paper addresses the following question: what is the essential difference between stochastic local search (LS) and systematic backtracking (BT) that gives LS superior scalability ? One possibility is LS's lack of firm commitment to any variable assignment. Three BT algorithms are modifi ..."
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Cited by 12 (4 self)
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This paper addresses the following question: what is the essential difference between stochastic local search (LS) and systematic backtracking (BT) that gives LS superior scalability ? One possibility is LS's lack of firm commitment to any variable assignment. Three BT algorithms are modified to have this feature by introducing randomness into the choice of backtracking variable: a forward checker for nqueens, the DSATUR graph colouring algorithm, and a DavisLogemannLoveland procedure for satisfiability. In each case the modified algorithm scales like LS and sometimes gives better results. It is argued that randomised backtracking is a form of local search.