The algorithm design manual
Vertex cover: further observations and further improvements
Journal of Algorithms
Ant Colony Optimization
A simple model to generate hard satisfiable instances
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Understanding the pheromone system within ant colony optimization
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Parameterized Complexity
A Stochastic Local Search Approach to Vertex Cover
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Local search with edge weighting and configuration checking heuristics for minimum vertex cover
Artificial Intelligence
Implementation and comparison of heuristics for the vertex cover problem on huge graphs
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
A hybridized tabu search approach for the minimum weight vertex cover problem
Journal of Heuristics
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For solving combinatorial optimisation problems, exact methods accurately exploit the structure of the problem but are tractable only up to a certain size; approximation or heuristic methods are tractable for very large problems but may possibly be led into a bad solution. A question that arises is, From where can we obtain knowledge of the problem structure via exact methods that can be exploited on large-scale problems by heuristic methods? We present a framework that allows the exploitation of existing techniques and resources to integrate such structural knowledge into the Ant Colony System metaheuristic, where the structure is determined through the notion of kernelization from the field of parameterized complexity. We give experimental results using vertex cover as the problem instance, and show that knowledge of this type of structure improves performance beyond previously defined ACS algorithms.