Generalized Simulated Annealing for Global Optimization: The GenSA Package

Abstract:

Many problems in statistics, finance, biology, pharmacology, physics, mathematics, eco nomics, and chemistry involve determination of the global minimum of multidimensional functions. R packages for different stochastic methods such as genetic algorithms and differential evolution have been developed and successfully used in the R community. Based on Tsallis statistics, the R package GenSA was developed for generalized simulated annealing to process complicated non-linear objective functions with a large number of local minima. In this paper we provide a brief introduction to the R package and demonstrate its utility by solving a non-convex portfolio optimization problem in finance and the Thomson problem in physics. GenSA is useful and can serve as a complementary tool to, rather than a replacement for, other widely used R packages for optimization.

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Published

June 2, 2013

Received

Nov 29, 2011

DOI

10.32614/RJ-2013-002

Volume

Pages

5/1

13 - 28

CRAN packages used

DEoptim, rgenoud, likelihood, dclone, subselect, GenSA

CRAN Task Views implied by cited packages

Optimization, HighPerformanceComputing, Bayesian, ChemPhys, gR, MachineLearning

Footnotes

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    Citation

    For attribution, please cite this work as

    Xiang, et al., "The R Journal: Generalized Simulated Annealing for Global Optimization: The GenSA Package", The R Journal, 2013

    BibTeX citation

    @article{RJ-2013-002,
      author = {Xiang, Yang and Gubian, Sylvain and Suomela, Brian and Hoeng, Julia},
      title = {The R Journal: Generalized Simulated Annealing for Global Optimization: The GenSA Package},
      journal = {The R Journal},
      year = {2013},
      note = {https://doi.org/10.32614/RJ-2013-002},
      doi = {10.32614/RJ-2013-002},
      volume = {5},
      issue = {1},
      issn = {2073-4859},
      pages = {13-28}
    }