SIAM Journal on Computing
Lower bounds and reduction procedures for the bin packing problem
Discrete Applied Mathematics - Combinatorial Optimization
Asynchronous organizations for multi-algorithm problems
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Synergy in cooperating agents: designing manipulators from task specifications
Synergy in cooperating agents: designing manipulators from task specifications
Primary production scheduling at steelmaking industries
IBM Journal of Research and Development
Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
ACM Computing Surveys (CSUR)
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Solving Multiple Knapsack Problems by Cutting Planes
SIAM Journal on Optimization
Algorithm portfolio design: theory vs. practice
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Electronic Commerce Research
A-Teams: An Agent Architecture for Optimization and Decision Support
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Fuzzy approach to multilevel knapsack problems
Computers & Mathematics with Applications
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For hard optimization problems, it is difficult to design heuristic algorithms which exhibit uniformly superior performance for all problem instances. As a result it becomes necessary to tailor the algorithms based on the problem instance. In this paper, we introduce the use of a cooperative problem solving team of heuristics that evolves algorithms for a given problem instance. The efficacy of this method is examined by solving six difficult instances of a bicriteria sparse multiple knapsack problem. Results indicate that such tailored algorithms uniformly improve solutions as compared to using predesigned heuristic algorithms.