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Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Eighteenth national conference on Artificial intelligence
Manpower scheduling with time windows
Proceedings of the 2003 ACM symposium on Applied computing
A Development Framework for Rapid Meta-Heuristics Hybridization
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Proceedings of the 2005 ACM symposium on Applied computing
Heuristic methods for graph coloring problems
Proceedings of the 2005 ACM symposium on Applied computing
Combining genetic algorithms with squeaky-wheel optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Improving genetic algorithm performance with intelligent mappings from chromosomes to solutions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Simulation-based planning for planetary rover experiments
WSC '05 Proceedings of the 37th conference on Winter simulation
Agent-based simulation for software project planning
WSC '05 Proceedings of the 37th conference on Winter simulation
YIELDS: A Yet Improved Limited Discrepancy Search for CSPs
CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Journal of Artificial Intelligence Research
Maximizing flexibility: a retraction heuristic for oversubscribed scheduling problems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Incomplete tree search using adaptive probing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The general yard allocation problem
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Expert Systems with Applications: An International Journal
A new neighborhood based on improvement graph for robust graph coloring problem
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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We describe a general approach to optimization which we term "Squeaky Wheel" Optimization (swo). In swo, a greedy algorithm is used to construct a solution which is then analyzed to find the trouble spots, i.e., those elements, that, if improved, are likely to improve the objective function score. That analysis is used to generate new priorities that determine the order in which the greedy algorithm constructs the next solution. This Construct/Analyze/Prioritize cycle continues until some limit is reached, or an acceptable solution is found.SWO can be viewed as operating on two search spaces: solutions and prioritizations. Successive solutions are only indirectly related, via the re-prioritization that results from analyzing the prior solution. Similarly, successive prioritizations are generated by constructing and analyzing solutions. This "coupled search" has some interesting properties, which we discuss.We report encouraging experimental results on two domains, scheduling problems that arise in fiber-optic cable manufacturing, and graph coloring problems. The fact that these domains are very different supports our claim that swo is a general technique for optimization.