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113
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 168 (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.
The Quest for Efficient Boolean Satisfiability Solvers
, 2002
"... has seen much interest in not just the theoretical computer science community, but also in areas where practical solutions to this problem enable significant practical applications. Since the first development of the basic search based algorithm proposed by Davis, Putnam, Logemann and Loveland (DPLL ..."
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Cited by 121 (2 self)
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has seen much interest in not just the theoretical computer science community, but also in areas where practical solutions to this problem enable significant practical applications. Since the first development of the basic search based algorithm proposed by Davis, Putnam, Logemann and Loveland (DPLL) about forty years ago, this area has seen active research effort with many interesting contributions that have culminated in stateoftheart SAT solvers today being able to handle problem instances with thousands, and in same cases even millions, of variables. In this paper we examine some of the main ideas along this passage that have led to our current capabilities. Given the depth of the literature in this field, it is impossible to do this in any comprehensive way; rather we focus on techniques with consistent demonstrated efficiency in available solvers. For the most part, we focus on techniques within the basic DPLL search framework, but also briefly describe other approaches and look at some possible future research directions. 1.
Effective Use of Boolean Satisfiability Procedures in the Formal Verification of Superscalar and VLIW Microprocessors
 Journal of Symbolic Computation
, 2001
"... We compare SATcheckers and decision diagrams on the evaluation of Boolean formulas produced in the formal verification of both correct and buggy versions of superscalar and VLIW microprocessors. We identify one SATchecker that significantly outperforms the rest. We evaluate ways to enhance its per ..."
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Cited by 86 (12 self)
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We compare SATcheckers and decision diagrams on the evaluation of Boolean formulas produced in the formal verification of both correct and buggy versions of superscalar and VLIW microprocessors. We identify one SATchecker that significantly outperforms the rest. We evaluate ways to enhance its performance by variations in the generation of the Boolean correctness formulas. We reassess optimizations previously used to speed up the formal verification and probe future challenges.
Backdoors to typical case complexity
, 2003
"... There has been significant recent progress in reasoning and constraint processing methods. In areas such as planning and finite modelchecking, current solution techniques can handle combinatorial problems with up to a million variables and five million constraints. The good scaling behavior of thes ..."
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Cited by 83 (12 self)
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There has been significant recent progress in reasoning and constraint processing methods. In areas such as planning and finite modelchecking, current solution techniques can handle combinatorial problems with up to a million variables and five million constraints. The good scaling behavior of these methods appears to defy what one would expect based on a worstcase complexity analysis. In order to bridge this gap between theory and practice, we propose a new framework for studying the complexity of these techniques on practical problem instances. In particular, our approach incorporates general structural properties observed in practical problem instances into the formal complexity
A Bayesian Approach to Tackling Hard Computational Problems
 IN UAI
, 2001
"... We describe research and results centering on the construction and use of Bayesian models that can predict the run time of problem solvers. Our efforts are motivated by observations of high variance in the time required to solve instances for several challenging problems. The methods ..."
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Cited by 63 (9 self)
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We describe research and results centering on the construction and use of Bayesian models that can predict the run time of problem solvers. Our efforts are motivated by observations of high variance in the time required to solve instances for several challenging problems. The methods
Dynamic Restart Policies
, 2002
"... We describe theoretical results and empirical study of contextsensitive restart policies for randomized search procedures. ..."
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Cited by 56 (5 self)
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We describe theoretical results and empirical study of contextsensitive restart policies for randomized search procedures.
Performance prediction and automated tuning of randomized and parametric algorithms
 In Proc. of CP06
, 2006
"... Abstract. Machine learning can be used to build models that predict the runtime of search algorithms for hard combinatorial problems. Such empirical hardness models have previously been studied for complete, deterministic search algorithms. In this work, we demonstrate that such models can also make ..."
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Cited by 54 (21 self)
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Abstract. Machine learning can be used to build models that predict the runtime of search algorithms for hard combinatorial problems. Such empirical hardness models have previously been studied for complete, deterministic search algorithms. In this work, we demonstrate that such models can also make surprisingly accurate predictions of the runtime distributions of incomplete and randomized search methods, such as stochastic local search algorithms. We also show for the first time how information about an algorithm’s parameter settings can be incorporated into a model, and how such models can be used to automatically adjust the algorithm’s parameters on a perinstance basis in order to optimize its performance. Empirical results for Novelty + and SAPS on structured and unstructured SAT instances show very good predictive performance and significant speedups of our automatically determined parameter settings when compared to the default and best fixed distributionspecific parameter settings. 1
A sharp threshold in proof complexity
 PROCEEDINGS OF STOC 2001
, 2001
"... We give the first example of a sharp threshold in proof complexity. More precisely, we show that for any sufficiently small � and � � �, random formulas consisting of 2clauses and 3clauses, which are known to be unsatisfiable almost certainly, almost certainly require resolution and DavisPutnam ..."
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Cited by 51 (13 self)
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We give the first example of a sharp threshold in proof complexity. More precisely, we show that for any sufficiently small � and � � �, random formulas consisting of 2clauses and 3clauses, which are known to be unsatisfiable almost certainly, almost certainly require resolution and DavisPutnam proofs of unsatisfiability of exponential size, whereas it is easily seen that random formulas with 2clauses (and 3clauses) have linear size proofs of unsatisfiability almost certainly. A consequence of our result also yields the first proof that typical random 3CNF formulas at ratios below the generally accepted range of the satisfiability threshold (and thus expected to be satisfiable almost certainly) cause natural DavisPutnam algorithms to take exponential time to find satisfying assignments.
Understanding Random SAT: Beyond the ClausestoVariables Ratio
 In Proc. of CP04
"... It is well known that the ratio of the number of clauses to the number of variables in a random kSAT instance is highly correlated with the instance's empirical hardness. We consider the problem of identifying such features of random SAT instances automatically with machine learning. We describe ..."
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Cited by 44 (17 self)
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It is well known that the ratio of the number of clauses to the number of variables in a random kSAT instance is highly correlated with the instance's empirical hardness. We consider the problem of identifying such features of random SAT instances automatically with machine learning. We describe and analyze models for three SAT solverskcnfs, oksolver and satzand for two different distributions of instances: uniform random 3SAT with varying ratio of clausestovariables, and uniform random 3SAT with fixed ratio of clausestovariables.