DEoptim: An R Package for Global Optimization by Differential Evolution

By Katharine M. Mullen, David Ardia, David Gil, D. Windover, J. Cline. 2009

831 citations recorded by the source corpus.

This article describes the R package DEoptim which implements the differential evolution algorithm for the global optimization of a real-valued function of a real-valued parameter vector. The implementation of differential evolution in DEoptim interfaces with C code for efficiency. The utility of the package is illustrated via case studies in fitting a Parratt model for X-ray reflectometry data and a Markov-Switching Generalized AutoRegressive Conditional Heteroskedasticity (MSGARCH) model for the returns of the Swiss Market Index.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ae1a26aaa9a75d99028408699c0c7b5f03063f77

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Citations in the corpus

A physiological analogy of the niche for projecting the potential distribution of plants (2012, methodology): “These predictions of M are made using the TTR model forced with environmental factors (Tmin, Tmean, Tmax, W, Nsoil, Q) and the b parameters were estimated using Price et al.’s (2006) differential evolution genetic algorithm (Mullen et al., 2011).”
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021, methodology): “Evolution strategies Popular R packages that implement different ES are rgenoud (Mebane Jr. & Sekhon, 2011), cmaes, adagio, DEoptim (Mullen et al., 2011), mco, and ecr (Bossek, 2017) and mosmafs (Binder, Moosbauer, et al., 2020a).”
Generalized Simulated Annealing for Global Optimization: The GenSA Package (2013, methodology): “DEoptim and rgenoud have been widely used to successfully solve optimization problems arising in diverse fields (Ardia et al., 2011; Mullen et al., 2011; Mebane Jr and Sekhon, 2011).”
Ecological efficiency assessment under the construction of low-carbon city: a perspective of green technology innovation (2021, positive): “…solutions for Model (2) is the Differential Evolution Optimization (DE Optimization) technique, which is a member of the genetic algorithm family and imitates the process of natural selection in an evolutionary way (further details can be seen from Ardia et al. (2011) and Mullen et al. (2011)).”
Generalized Simulated Annealing (2017, background): “These advantages make the use of R preferable in many situations to other programming languages, such as Java, C++, Fortran, and Pascal [16].”
Surrogate-Assisted Learning of Neural Networks (2017, methodology): “• DEoptim [13] is an R-implementation of the di erential evolution algorithm [19], which belongs to the class of evolutionary algorithms.”