ISSN 0253-2778

CN 34-1054/N

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Evolutionary algorithm portfolios based on information sharing

  • University of Science and Technology of China,School of Information Science and Technology,Hefei,230026

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.01.002
  • Received Date: 14 April 2017
  • Rev Recd Date: 05 August 2017
  • Publish Date: 31 January 2018
    Abstract

  • Abstract

    A general framework for combining multiple evolutionary algorithms EAP_IS is proposed. Each of the constituent algorithms in this framework has its own population to maintain its characteristic and the continuity of the evolution process. EAP_IS runs each constituent algorithm with a part of the given time budget and encourages information sharing among the constituent algorithms. The effectiveness of EAP_IS has been verified by investigating 26 instantiations of it on 25 benchmark functions, and further comparisons of EAP_IS with other combinatorial frameworks have been conducted. Experimental results show that the proposed framework can improve the performance of constituent algorithms effectively.

    Abstract

    A general framework for combining multiple evolutionary algorithms EAP_IS is proposed. Each of the constituent algorithms in this framework has its own population to maintain its characteristic and the continuity of the evolution process. EAP_IS runs each constituent algorithm with a part of the given time budget and encourages information sharing among the constituent algorithms. The effectiveness of EAP_IS has been verified by investigating 26 instantiations of it on 25 benchmark functions, and further comparisons of EAP_IS with other combinatorial frameworks have been conducted. Experimental results show that the proposed framework can improve the performance of constituent algorithms effectively.
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    • References(23)
  • [1]
    HOLLAND J H. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence[J]. Quarterly Review of Biology, 1992, 6(2): 126-137.
    [2]
    EBERHART R, KENNEDY J. A new optimizer using particle swarm theory[C]// International Symposium on MICRO Machine and Human Science. Nagoya: IEEE Press, 1995: 39-43.
    [3]
    STORN R, PRICE K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359.
    [4]
    LARRAAGA, P, LOZANO J A Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation[M]. Springer Science & Business Media, 2001.
    [5]
    KARABOGA D, BASTURK B. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm[J]. Journal of Global Optimization, 2007, 39(3): 459-471.
    [6]
    TALBI E G. A taxonomy of hybrid metaheuristics [J]. Journal of Heuristics, 2002, 8(5): 541-564.
    [7]
    VRUGT J A, ROBINSON B A, HYMAN J M. Self-adaptive multimethod search for global optimization in real-parameter spaces [J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2): 243-259.
    [8]
    VRUGT J A, ROBINSON B A. Improved evolutionary optimization from genetically adaptive multimethod search[J]. Proceedings of the National Academy of Sciences, 2007, 104(3): 708-711.
    [9]
    YUEN S Y, CHOW C K, ZHANG X. Which algorithm should I choose at any point of the search: An evolutionary portfolio approach[C] // Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation. Amsterdam: ACM Press, 2013: 567-574.
    [10]
    MOLINA D, LOZANO M, HERRERA F. MA-SW-Chains: Memetic algorithm based on local search chains for large scale continuous global optimization [C]// Proceedings of the IEEE Congress on Evolutionary Computation. Barcelona: ACM Press, 2010: 1-8.
    [11]
    PENG F, TANG K, CHEN G, et al. Population-based algorithm portfolios for numerical optimization[J]. IEEE Transactions on Evolutionary Computation, 2010, 14(5): 782-800.
    [12]
    TANG K, PENG F, CHEN G, et al. Population-based algorithm portfolios with automated constituent algorithms selection[J]. Information Sciences, 2014, 27(1)9: 94-104.
    [13]
    AKAY R, BASTURK A, KALINLI A, et al. Parallel population-based algorithm portfolios: An empirical study[J]. Neurocomputing, 2017, 247(C): 115-125.
    [14]
    SOURAVLIAS D, PARSOPOULOS K E, ALBA E. Parallel algorithm portfolio with market trading-based time allocation[C]// Proceedings of the Operations Research. Springer, 2016: 567-574.
    [15]
    GONG W, ZHOU A, CAI Z. A multioperator search strategy based on cheap surrogate models for evolutionary optimization[J]. IEEE Transactions on Evolutionary Computation, 2015, 19(5): 746-758.
    [16]
    Goldberg D E, Richardson J. Genetic algorithms with sharing for multimodal function optimization[C]// Proceedings of the 2nd International Conference on Genetic Algorithms. Cambridge, USA: Hillsdale, 1987: 41-49.
    [17]
    HANSEN N. The CMA evolution strategy: A comparing review[A]// Study in Fuzziness & Soft Computing. Springe, 2006, 192: 75-102.
    [18]
    ZAMBRANOBIGIARINI M, CLERC M, ROJAS R. Standard particle swarm optimisation 2011 at CEC-2013: A baseline for future PSO improvements[C]// IEEE Congress on Evolutionary Computation, 2013:2337-2344.
    [19]
    QIN A K, HUANG V L, SUGANTHAN P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2):398-417.
    [20]
    WANG Y, CAI Z, ZHANG Q. Differential evolution with composite trial vector generation strategies and control parameters[J]. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 55-66.
    [21]
    SUGANTHAN P N, HANSEN N, LIANG J J, et al. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization[R]. KanGAL Report, NCL-TR-2005001, 2005.
    [22]
    WILCOXON F. Individual comparisons by ranking methods[J]. Biometrics Bulletin, 1945, 1(6): 80-83.
    [23]
    DE LA FUENTE A L T, SáNCHEZ J M P. A framework for hybrid dynamic evolutionary algorithms: Multiple offspring sampling (MOS)[D]. Universidad Politécnica de Madrid,2009.
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    [1]
    HOLLAND J H. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence[J]. Quarterly Review of Biology, 1992, 6(2): 126-137.
    [2]
    EBERHART R, KENNEDY J. A new optimizer using particle swarm theory[C]// International Symposium on MICRO Machine and Human Science. Nagoya: IEEE Press, 1995: 39-43.
    [3]
    STORN R, PRICE K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359.
    [4]
    LARRAAGA, P, LOZANO J A Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation[M]. Springer Science & Business Media, 2001.
    [5]
    KARABOGA D, BASTURK B. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm[J]. Journal of Global Optimization, 2007, 39(3): 459-471.
    [6]
    TALBI E G. A taxonomy of hybrid metaheuristics [J]. Journal of Heuristics, 2002, 8(5): 541-564.
    [7]
    VRUGT J A, ROBINSON B A, HYMAN J M. Self-adaptive multimethod search for global optimization in real-parameter spaces [J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2): 243-259.
    [8]
    VRUGT J A, ROBINSON B A. Improved evolutionary optimization from genetically adaptive multimethod search[J]. Proceedings of the National Academy of Sciences, 2007, 104(3): 708-711.
    [9]
    YUEN S Y, CHOW C K, ZHANG X. Which algorithm should I choose at any point of the search: An evolutionary portfolio approach[C] // Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation. Amsterdam: ACM Press, 2013: 567-574.
    [10]
    MOLINA D, LOZANO M, HERRERA F. MA-SW-Chains: Memetic algorithm based on local search chains for large scale continuous global optimization [C]// Proceedings of the IEEE Congress on Evolutionary Computation. Barcelona: ACM Press, 2010: 1-8.
    [11]
    PENG F, TANG K, CHEN G, et al. Population-based algorithm portfolios for numerical optimization[J]. IEEE Transactions on Evolutionary Computation, 2010, 14(5): 782-800.
    [12]
    TANG K, PENG F, CHEN G, et al. Population-based algorithm portfolios with automated constituent algorithms selection[J]. Information Sciences, 2014, 27(1)9: 94-104.
    [13]
    AKAY R, BASTURK A, KALINLI A, et al. Parallel population-based algorithm portfolios: An empirical study[J]. Neurocomputing, 2017, 247(C): 115-125.
    [14]
    SOURAVLIAS D, PARSOPOULOS K E, ALBA E. Parallel algorithm portfolio with market trading-based time allocation[C]// Proceedings of the Operations Research. Springer, 2016: 567-574.
    [15]
    GONG W, ZHOU A, CAI Z. A multioperator search strategy based on cheap surrogate models for evolutionary optimization[J]. IEEE Transactions on Evolutionary Computation, 2015, 19(5): 746-758.
    [16]
    Goldberg D E, Richardson J. Genetic algorithms with sharing for multimodal function optimization[C]// Proceedings of the 2nd International Conference on Genetic Algorithms. Cambridge, USA: Hillsdale, 1987: 41-49.
    [17]
    HANSEN N. The CMA evolution strategy: A comparing review[A]// Study in Fuzziness & Soft Computing. Springe, 2006, 192: 75-102.
    [18]
    ZAMBRANOBIGIARINI M, CLERC M, ROJAS R. Standard particle swarm optimisation 2011 at CEC-2013: A baseline for future PSO improvements[C]// IEEE Congress on Evolutionary Computation, 2013:2337-2344.
    [19]
    QIN A K, HUANG V L, SUGANTHAN P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2):398-417.
    [20]
    WANG Y, CAI Z, ZHANG Q. Differential evolution with composite trial vector generation strategies and control parameters[J]. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 55-66.
    [21]
    SUGANTHAN P N, HANSEN N, LIANG J J, et al. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization[R]. KanGAL Report, NCL-TR-2005001, 2005.
    [22]
    WILCOXON F. Individual comparisons by ranking methods[J]. Biometrics Bulletin, 1945, 1(6): 80-83.
    [23]
    DE LA FUENTE A L T, SáNCHEZ J M P. A framework for hybrid dynamic evolutionary algorithms: Multiple offspring sampling (MOS)[D]. Universidad Politécnica de Madrid,2009.
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