A network-related nuclear power model with an intelligent branch-and-bound solution approach
Annals of Operations Research
Computers and Operations Research
A hybrid heuristic to solve a task allocation problem
Computers and Operations Research
Tabu Search
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Exact Solutions to Task Allocation Problems
Management Science
Classification by vertical and cutting multi-hyperplane decision tree induction
Decision Support Systems
Using datamining techniques to help metaheuristics: a short survey
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
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Metaheuristic approaches based on the neighborhood search escape local optimality by applying predefined rules and constraints, such as tabu restrictions (in tabu search), acceptance criteria (in simulated annealing), and shaking (in variable neighborhood search). We propose a general approach that attempts to learn (off-line) the guiding constraints that, when applied online, will result in effective escape directions from local optima. Given a class of problems, the learning process is performed off-line, and the results are applied to constrained neighborhood searches to guide the solution process out of local optimality. Computational results on the constrained task allocation problem show that adding these guiding constraints to a simple tabu search improves the quality of the solutions found, making the overall method competitive with state-of-the-art methods for this class of problems. We also present a second set of tests on the matrix bandwidth minimization problem.