Autocorrelation coefficient for the graph bipartitioning problem
Theoretical Computer Science
On the quality of local search for the quadratic assignment problem
Discrete Applied Mathematics
P-Complete Approximation Problems
Journal of the ACM (JACM)
On the classification of NP-complete problems in terms of their correlation coefficient
Proceedings of the 5th Twente workshop on on Graphs and combinatorial optimization
On the landscape ruggedness of the quadratic assignment problem
Theoretical Computer Science
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
SIAM Review
Cut Size Statistics of Graph Bisection Heuristics
SIAM Journal on Optimization
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Meta-heuristics are a powerful way to approximately solve hard combinatorial optimization problems. However, for a problem, the quality of results can vary considerably from one instance to another. Understanding such a behaviour is important from a theoretical point of view, but also has practical applications such as for the generation of instances during the evaluation stage of a heuristic.In this paper we propose a new complexity measure for the Quadratic Assignment Problem in the context of metaheuristics based on local search, e.g. simulated annealing. We show how the ruggedness coefficient previously introduced by the authors, in conjunction with the well known concept of dominance, provides important features of the search space explored during a local search algorithm, and gives a rather precise idea of the complexity of an instance for these heuristics. We comment previous experimental studies concerning tabu search methods and genetic algorithms with local search in the light of our complexity measure. New computational results with simulated annealing and taboo search are presented.