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Bioinspired computation in combinatorial optimization: algorithms and their computational complexity

Published: 06 July 2013 Publication History
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References

[1]
F. Neumann and C. Witt (2010): Bioinspired Computation in Combinatorial Optimization -- Algorithms and Their Computational Complexity. Springer.
[2]
A. Auger and B. Doerr (2011): Theory of Randomized Search Heuristics -- Foundations and Recent Developments. World Scientific Publishing
[3]
F. Neumann and I. Wegener (2007): Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. Theoretical Computer Science 378(1):32--40.
[4]
O. Giel and I. Wegener (2003): Evolutionary algorithms and the maximum matching problem. Proc. of STACS '03, LNCS 2607, 415--426, Springer
[5]
B. Doerr, E. Happ and C. Klein (2012): Crossover can provably be useful in evolutionary computation. Theoretical Computer Science 425:17--33.
[6]
C. Witt (2005): Worst-case and average-case approximations by simple randomized search heuristics. Proc. of STACS 2005, LNCS 3404, 44--56, Springer.
[7]
T. Friedrich, J. He, N. Hebbinghaus, F. Neumann and C. Witt (2010): Approximating covering problems by randomized search heuristics using multi-objective models. Evolutionary Computation 18(4):617--633.
[8]
S. Kratsch and F. Neumann (2009): Fixed-parameter evolutionary algorithms and the vertex cover problem. In Proc. of GECCO 2009, 293--300. ACM.
[9]
A. M. Sutton and F. Neumann (2012): A parameterized runtime analysis of evolutionary algorithms for the Euclidean traveling salesperson problem. Proc. of AAAI 2012.

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    Published In

    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 06 July 2013

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    Author Tags

    1. bioinspired computing
    2. combinatorial optimization
    3. evolutionary algorithms

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    GECCO '13
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    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2024)A Comprehensive Survey on Bio-Inspired Optimization Routing Algorithms for Vehicular Ad-hoc Networks2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC61858.2024.10714651(694-699)Online publication date: 3-Oct-2024
    • (2024)Analysis of Evolutionary Diversity Optimisation for the Maximum Matching ProblemParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_10(149-165)Online publication date: 14-Sep-2024
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    • (2021)Nature-Inspired Metaheuristic Techniques for Combinatorial Optimization Problems: Overview and Recent AdvancesMathematics10.3390/math92026339:20(2633)Online publication date: 19-Oct-2021
    • (2021)Solving nonlinear systems and unconstrained optimization problems by hybridizing whale optimization algorithm and flower pollination algorithmMathematics and Computers in Simulation10.1016/j.matcom.2021.07.010Online publication date: Jul-2021
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