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.
DEoptim, rgenoud, likelihood, dclone, subselect, GenSA
Optimization, HighPerformanceComputing, Bayesian, ChemPhys, gR, MachineLearning
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For attribution, please cite this work as
Xiang, et al., "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 = {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}
}