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    Annealing evolutionary stochastic approximation Monte Carlo for global optimization

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    Type
    Article
    Authors
    Liang, Faming
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2010-04-08
    Online Publication Date
    2010-04-08
    Print Publication Date
    2011-07
    Permanent link to this record
    http://hdl.handle.net/10754/597576
    
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    Abstract
    In this paper, we propose a new algorithm, the so-called annealing evolutionary stochastic approximation Monte Carlo (AESAMC) algorithm as a general optimization technique, and study its convergence. AESAMC possesses a self-adjusting mechanism, whose target distribution can be adapted at each iteration according to the current samples. Thus, AESAMC falls into the class of adaptive Monte Carlo methods. This mechanism also makes AESAMC less trapped by local energy minima than nonadaptive MCMC algorithms. Under mild conditions, we show that AESAMC can converge weakly toward a neighboring set of global minima in the space of energy. AESAMC is tested on multiple optimization problems. The numerical results indicate that AESAMC can potentially outperform simulated annealing, the genetic algorithm, annealing stochastic approximation Monte Carlo, and some other metaheuristics in function optimization. © 2010 Springer Science+Business Media, LLC.
    Citation
    Liang F (2010) Annealing evolutionary stochastic approximation Monte Carlo for global optimization. Stat Comput 21: 375–393. Available: http://dx.doi.org/10.1007/s11222-010-9176-1.
    Sponsors
    The author's research was supported in part by the grant (DMS-0607755) made by the National Science Foundation and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). The author thanks the editor, the associate editor and the referees for their comments which have led to significant improvement of this paper.
    Publisher
    Springer Nature
    Journal
    Statistics and Computing
    DOI
    10.1007/s11222-010-9176-1
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11222-010-9176-1
    Scopus Count
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