Generalized Simulated Annealing for Global Optimization: The GenSA Package

By Y. Xiang, Sylvain Gubian, Brian P. Suomela, Julia Hoeng. The R Journal, 2013

363 citations recorded by the source corpus.

Many problems in statistics, finance, biology, pharmacology, physics, mathematics, economics, 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.

Subfields: Economics and Econometrics, Finance, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8e15aab28534a7cf64ceeaeed8e927f407212f84

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Marc H. Taylor
2. T. Mildenberger

In Finance

1. Thomas Bartz-Beielstein

In Statistical and Nonlinear Physics

1. Marc H. Taylor
2. T. Mildenberger

Citations in the corpus

• Surrogate-Assisted Learning of Neural Networks (2017, background): “• GenSA provides a version of generalized simulated annealing [20].”
• Assessing sources of uncertainty in length-based estimates of body growth in populations of fishes and macroinvertebrates with bootstrapped ELEFAN (2018, methodology): “, 2017) uses common simulated annealing (Xiang et al., 2013), while ELEFAN_GA (Mildenberger et al.”
• Generalized Simulated Annealing (2017, methodology): “The package GenSA has proven to be a useful tool for solving global optimization problems [17].”