DEoptim

DEoptim: An R Package for Global Optimization by Differential Evolution. 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.


References in zbMATH (referenced in 49 articles , 1 standard article )

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  1. Mohammadi, Hossein; Challenor, Peter; Williamson, Daniel; Goodfellow, Marc: Cross-validation-based adaptive sampling for Gaussian process models (2022)
  2. You, Kisung; Suh, Changhee: Parameter estimation and model-based clustering with spherical normal distribution on the unit hypersphere (2022)
  3. Augustyniak, Maciej; Godin, Frédéric; Hamel, Emmanuel: A mixed bond and equity fund model for the valuation of variable annuities (2021)
  4. Ozan Evkaya, O.; Yozgatlıgil, Ceylan; Sevtap Selcuk-Kestel, A.: CD-vine model for capturing complex dependence (2021)
  5. Alexander Lange, Bernhard Dalheimer, Helmut Herwartz, Simone Maxand: svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis (2020) not zbMATH
  6. Boudt, Kris; Wan, Chunlin: The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization (2020)
  7. He, Yunhao; Leippold, Markus: Short-run risk, business cycle, and the value premium (2020)
  8. Kleisinger-Yu, Xi; Komaric, Vlatka; Larsson, Martin; Regez, Markus: A multifactor polynomial framework for long-term electricity forwards with delivery period (2020)
  9. Shakhatreh, Mohammed K.; Lemonte, Artur J.; Cordeiro, Gauss M.: On the generalized extended exponential-Weibull distribution: properties and different methods of estimation (2020)
  10. Blostein, Martin; Miljkovic, Tatjana: On modeling left-truncated loss data using mixtures of distributions (2019)
  11. Castillo-Páez, Sergio; Fernández-Casal, Rubén; García-Soidán, Pilar: A nonparametric bootstrap method for spatial data (2019)
  12. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  13. David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
  14. Cano-Berlanga, Sebastián; Giménez-Gómez, José-Manuel: On Chinese stock markets: how have they evolved over time? (2018)
  15. Dünder, Emre; Gümüştekin, Serpil; Murat, Naci; Cengiz, Mehmet Ali: Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm (2018)
  16. Eckert, Johanna; Gatzert, Nadine: Risk- and value-based management for non-life insurers under solvency constraints (2018)
  17. Levantesi, Susanna; Menzietti, Massimiliano: Natural hedging in long-term care insurance (2018)
  18. Salehi, Mahdi; Azzalini, Adelchi: On application of the univariate Kotz distribution and some of its extensions (2018)
  19. Serrano-Rubio, Juan Pablo; Hernández-Aguirre, Arturo; Herrera-Guzmán, Rafael: An evolutionary algorithm using spherical inversions (2018)
  20. Thongsook, Saranya: Using the GA package in R program and desirability function to develop a multiple response optimization procedure in case of two responses (2018)

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