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116
A General Approach for Constraint Solving by Local Search
 In CPAIOR’00
, 2000
"... In this paper, we present a general approach for solving constraint problems by local search. The proposed approach is based on a set of highlevel constraint primitives motivated by constraint programming systems. These constraints constitute the basic bricks to formulate a given combinatorial prob ..."
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Cited by 29 (2 self)
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In this paper, we present a general approach for solving constraint problems by local search. The proposed approach is based on a set of highlevel constraint primitives motivated by constraint programming systems. These constraints constitute the basic bricks to formulate a given combinatorial problem. A tabu search engine ensures the resolution of the problem such formulated. Experimental results are shown to validate the proposed approach.
An application of Iterated Local Search to Graph Coloring Problem
 PROCEEDINGS OF THE COMPUTATIONAL SYMPOSIUM ON GRAPH COLORING AND ITS GENERALIZATIONS
, 2002
"... Graph coloring is a well known problem from graph theory that, when solving it with local search algorithms, is typically treated as a series of constraint satisfaction problems: for a given number of colors k, one has to find a feasible coloring; once such a coloring is found, the number of colo ..."
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Cited by 27 (2 self)
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Graph coloring is a well known problem from graph theory that, when solving it with local search algorithms, is typically treated as a series of constraint satisfaction problems: for a given number of colors k, one has to find a feasible coloring; once such a coloring is found, the number of colors is decreased and the local search starts again. Here we explore the application of Iterated Local Search to the graph coloring problem. Iterated Local Search is a simple and powerful metaheuristic that has shown very good results for a variety of optimization problems. In our research we investigate different perturbation schemes and present computational results on some hard instances from the DIMACS benchmark suite.
Breaking instanceindependent symmetries in exact graph coloring
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 2004
"... Code optimization and high level synthesis can be posed as constraint satisfaction and optimization problems, such as graph coloring used in register allocation. Naturallyoccurring instances of such problems are often small and can be solved optimally. A recent wave of improvements in algorithms for ..."
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Cited by 17 (5 self)
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Code optimization and high level synthesis can be posed as constraint satisfaction and optimization problems, such as graph coloring used in register allocation. Naturallyoccurring instances of such problems are often small and can be solved optimally. A recent wave of improvements in algorithms for Boolean satisfiability (SAT) and 01 ILP suggests generic problemreduction methods, rather than problemspecific heuristics, because (1) heuristics are easily upset by new constraints, (2) heuristics tend to ignore structure, and (3) many relevant problems are provably inapproximable. The NPspec project offers a language to specify NPproblems and automatic reductions to SAT. Problem reductions often lead to highly symmetric SAT instances, and symmetries are known to slow down SAT solvers. In this work, we compare several avenues for symmetrybreaking, in particular when certain kinds of symmetry are present in all generated instances. Our surprising conclusion is that instanceindependent symmetries should often be processed together with instancespecific symmetries rather than earlier, at the specification level.
A hybrid immune algorithm with information gain for the graph coloring problem
 In Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI, GECCO’03
, 2003
"... Abstract. We present a new Immune Algorithm that incorporates a simple local search procedure to improve the overall performances to tackle the graph coloring problem instances. We characterize the algorithm and set its parameters in terms of Information Gain. Experiments will show that the IA we pr ..."
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Cited by 16 (7 self)
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Abstract. We present a new Immune Algorithm that incorporates a simple local search procedure to improve the overall performances to tackle the graph coloring problem instances. We characterize the algorithm and set its parameters in terms of Information Gain. Experiments will show that the IA we propose is very competitive with the best evolutionary algorithms.
Stochastic local search algorithms for the graph set Tcolouring . . .
 APPROXIMATION ALGORITHMS AND METAHEURISTICS; COMPUTER AND INFORMATION SCIENCE SERIES
, 2005
"... The graph set Tcolouring problem (GSTCP) generalises the classical graph colouring problem; it asks for the assignment of sets of integers to the vertices of a graph such that constraints on the separation of any two numbers assigned to a single vertex or to adjacent vertices are satisfied and some ..."
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Cited by 13 (3 self)
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The graph set Tcolouring problem (GSTCP) generalises the classical graph colouring problem; it asks for the assignment of sets of integers to the vertices of a graph such that constraints on the separation of any two numbers assigned to a single vertex or to adjacent vertices are satisfied and some objective function is optimised. Among the objective functions of interest is the minimisation of the difference between the largest and the smallest integers used (the span). In this article, we present an experimental study of local search algorithms for solving general and large size instances of the GSTCP. We compare the performance of previously known as well as new algorithms covering both simple construction heuristics and elaborated stochastic local search algorithms. We investigate systematically different models and search strategies in the algorithms and determine the best choices for different types of instance. The study is an example of design of effective local search for constraint optimisation problems.
Multiple Objective Metaheuristic Algorithms For Combinatorial Optimization
, 2001
"... this paper. In particular they used a neighborhood operator and iterative improvement not used in our adaptation of MOGLS ..."
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Cited by 12 (2 self)
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this paper. In particular they used a neighborhood operator and iterative improvement not used in our adaptation of MOGLS
Scatter Search for Graph Coloring
"... In this paper, we present a first scatter search approach for the Graph Coloring Problem (GCP). The evolutionary strategy scatter search operates on a set of configurations by combining two or more elements. New configurations are improved before replacing others according to their quality (fitness) ..."
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Cited by 11 (4 self)
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In this paper, we present a first scatter search approach for the Graph Coloring Problem (GCP). The evolutionary strategy scatter search operates on a set of configurations by combining two or more elements. New configurations are improved before replacing others according to their quality (fitness), and sometimes, to their diversity. Scatter search has been applied recently to some combinatorial optimization problems with promising results. Nevertheless, it seems that no attempt of scatter search has been published for the GCP. This paper presents such an investigation and reports experimental results on some wellstudied DIMACS graphs.
Tutorial on surrogate constraint approaches for optimization in graphs
 Journal of Heuristics
, 2003
"... Surrogate constraint methods have been embedded in a variety of mathematical programming applications over the past thirty years, yet their potential uses and underlying principles remain incompletely understood by a large segment of the optimization community. In a number of significant domains of ..."
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Cited by 11 (4 self)
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Surrogate constraint methods have been embedded in a variety of mathematical programming applications over the past thirty years, yet their potential uses and underlying principles remain incompletely understood by a large segment of the optimization community. In a number of significant domains of combinatorial optimization, researchers have produced solution strategies without recognizing that they can be derived as special instances of surrogate constraint methods. Once the connection to surrogate constraint ideas is exposed, additional ways to exploit this framework become visible, frequently offering opportunities for improvement. We provide a tutorial on surrogate constraint approaches for optimization in graphs, illustrating the key ideas by reference to independent set and graph coloring problems, including constructions for weighted independent sets which have applications to associated covering and weighted maximum clique problems. In these settings, the surrogate constraints can be generated relative to wellknown packing and covering formulations that are convenient for exposing key notions. The surrogate constraint approaches yield widely used heuristics for
An experimental investigation of iterated local search for coloring graphs
 Applications of Evolutionary Computing, volume 2270 of LNCS
, 2002
"... Abstract. Graph coloring is a well known problem from graph theory that, when attacldng it with local search algorithms, is typically treated as a series of constraint satisfaction problems: for a given number of colors k one has to find a feasible coloring: once such a coloring is found, the number ..."
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Cited by 10 (4 self)
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Abstract. Graph coloring is a well known problem from graph theory that, when attacldng it with local search algorithms, is typically treated as a series of constraint satisfaction problems: for a given number of colors k one has to find a feasible coloring: once such a coloring is found, the number of colors is decreased and the local search starts again. Here we explore the application of Iterated Local Search on the graph coloring problem. Iterated Local Search is a simple and powerful metaheuristic that has shown very good results for a variety of optimization problems. In our research we investigated several perturbation schemes and present computational results on a widely used set of benchmarks problems, a subset of those available from the DIMACS benchmark suite. Our results suggest that Iterated Local Search is particularly promising on hard, structured graphs.
Scatter Search and Path Relinking: Advances and Applications
"... Scatter search (SS) is a populationbased method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, SS uses strategies fo ..."
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Cited by 10 (0 self)
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Scatter search (SS) is a populationbased method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, SS uses strategies for combining solution vectors that have proved effective in a variety of problem settings. Path relinking (PR) has been suggested as an approach to integrate intensification and diversification strategies in a search scheme. The approach may be viewed as an extreme (highly focused) instance of a strategy that seeks to incorporate attributes of high quality solutions, by creating inducements to favor these attributes in the moves selected. The goal of this paper is to examine SS and PR strategies that provide useful alternatives to more established search methods. We describe the features of SS and PR that set them apart from other evolutionary approaches, and that offer opportunities for creating increasingly more versatile and effective methods in the future. Specific applications are summarized to provide a clearer understanding of settings where the methods are being used.