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Analysis of greedy heuristics and weight-coded eas for multidimensional knapsack problems and multi-unit combinatorial auctions

Published: 07 July 2007 Publication History

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References

[1]
R. C. Holte. Combinatorial auctions, knapsack problems, and hill-climbing search. In E. Stroulia and S. Matwin, editors, Canadian Conference on AI, pages 57--66. Springer, 2001.
[2]
K. Leyton-Brown, Y. Shoham, and M. Tennenholtz. An algorithm for multi-unit combinatorial auctions. In Proceedings of the AAAI and IAAI, pages 56--61, Menlo Park, CA, 2000. AAAI Press.
[3]
G. Raidl. Weight-codings in a genetic algorithm for the multiconstraint knapsack problem. In Proceedings of the Congress on Evolutionary Computation, volume 1, pages 596--603. IEEE Press, 1999.
[4]
G. Raidl and J. Gottlieb. Empirical analysis of locality, heritability and heuristic bias in evolutionary algorithms: A case study for the multidimensional knapsack problem. Evolutionary Computation, 13, issue 4:441--475, 2005.

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  1. Analysis of greedy heuristics and weight-coded eas for multidimensional knapsack problems and multi-unit combinatorial auctions

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        cover image ACM Conferences
        GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
        July 2007
        2313 pages
        ISBN:9781595936974
        DOI:10.1145/1276958

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        Association for Computing Machinery

        New York, NY, United States

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        Published: 07 July 2007

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        1. multidimensional knapsack problem

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        GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
        Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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        • (2024)Branch-and-Bound and Dynamic Programming Approaches for the Knapsack ProblemOperations Research Forum10.1007/s43069-024-00372-25:4Online publication date: 17-Oct-2024
        • (2023)Nature-inspired algorithms for 0-1 knapsack problem: A surveyNeurocomputing10.1016/j.neucom.2023.126630554(126630)Online publication date: Oct-2023
        • (2022)SCARProceedings of the VLDB Endowment10.14778/3551793.355185015:11(3031-3044)Online publication date: 1-Jul-2022
        • (2019)A Comparative Study of Meta-Heuristic Optimization Algorithms for 0 – 1 Knapsack Problem: Some Initial ResultsIEEE Access10.1109/ACCESS.2019.29084897(43979-44001)Online publication date: 2019
        • (2018)The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic ApproachesAmerican Journal of Operations Research10.4236/ajor.2018.8502308:05(395-439)Online publication date: 2018
        • (2018)A hybrid genetic algorithm for solving 0/1 Knapsack ProblemProceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications10.1145/3230905.3230907(1-6)Online publication date: 2-May-2018
        • (2017)Robust Spectral Clustering for Noisy DataProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/3097983.3098156(737-746)Online publication date: 13-Aug-2017
        • (2015)Biased random-key genetic algorithms for the winner determination problem in combinatorial auctionsEvolutionary Computation10.1162/EVCO_a_0013823:2(279-307)Online publication date: 1-Jun-2015
        • (2011)A genetic algorithm hybridized with the discrete lagrangian method for trap escapingProceedings of the 5th international conference on Learning and Intelligent Optimization10.1007/978-3-642-25566-3_26(351-363)Online publication date: 17-Jan-2011
        • (2010)Using messy genetic algorithms for solving the winner determination problemProceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830810(1825-1832)Online publication date: 7-Jul-2010
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