The R Journal: article published in 2013, volume 5:1

Generalized Simulated Annealing for Global Optimization: The GenSA Package
Yang Xiang, Sylvain Gubian, Brian Suomela and Julia Hoeng , The R Journal (2013) 5:1, pages 13-28.

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.

Received: 2011-11-29; online 2013-06-03
CRAN packages: DEoptim, rgenoud, likelihood, dclone, subselect, GenSA
CRAN Task Views implied by cited CRAN packages: Optimization, HighPerformanceComputing, Bayesian, ChemPhys, gR, MachineLearning

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

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