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Artificial Intelligence
The complexity of the Lin-Kernighan heuristic for the traveling salesman problem
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Exploiting the deep structure of constraint problems
Artificial Intelligence
Sequential circuit test generation in a genetic algorithm framework
DAC '94 Proceedings of the 31st annual Design Automation Conference
Generating hard satisfiability problems
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
On the conversion between non-binary constraint satisfaction problems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
CRIS: a test cultivation program for sequential VLSI circuits
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A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Journal of the ACM (JACM)
Chaff: engineering an efficient SAT solver
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Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Counting Models Using Connected Components
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Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
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STOC '76 Proceedings of the eighth annual ACM symposium on Theory of computing
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Property analysis of symmetric travelling salesman problem instances acquired through evolution
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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ACM Computing Surveys (CSUR)
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Computers and Operations Research
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Annals of Mathematics and Artificial Intelligence
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EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Challenging heuristics: evolving binary constraint satisfaction problems
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This paper demonstrates how evolutionary computation can be used to acquire difficult to solve combinatorial problem instances. As a result of this technique, the corresponding algorithms used to solve these instances are stress-tested. The technique is applied in three important domains of combinatorial optimisation, binary constraint satisfaction, Boolean satisfiability, and the travelling salesman problem. The problem instances acquired through this technique are more difficult than the ones found in popular benchmarks. In this paper, these evolved instances are analysed with the aim to explain their difficulty in terms of structural properties, thereby exposing the weaknesses of corresponding algorithms.