R package rgenoud: R version of GENetic Optimization Using Derivatives. A genetic algorithm plus derivative optimizer. Genoud is a function that combines evolutionary search algorithms with derivative-based (Newton or quasi-Newton) methods to solve difficult optimization problems. Genoud may also be used for optimization problems for which derivatives do not exist. Genoud , via the cluster option, supports the use of multiple computers, CPUs or cores to perform parallel computations.

References in zbMATH (referenced in 26 articles )

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  1. Chang, Chung; Hsieh, Meng-Ke; Chiang, An Jen; Tsai, Yi-Hsuan; Liu, Chia-Chiung; Chen, Jiabin: Methods for estimating the optimal number and location of cut points in multivariate survival analysis: a statistical solution to the controversial effect of BMI (2019)
  2. Mickaël Binois and Victor Picheny: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis (2019) not zbMATH
  3. Chadsuthi, Sudarat; Wichapeng, Surapa: The modelling of hand, foot, and mouth disease in contaminated environments in Bangkok, Thailand (2018)
  4. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  5. 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)
  6. Zhang, Baqun; Zhang, Min: Variable selection for estimating the optimal treatment regimes in the presence of a large number of covariates (2018)
  7. Barrio, Irantzu; Rodríguez-Álvarez, María Xosé; Meira-Machado, Luis; Esteban, Cristóbal; Arostegui, Inmaculada: Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies (2017)
  8. Thongsook, Saranya: Using the GA package in R program and desirability function to develop a multiple response optimization procedure in case of two responses (2017)
  9. Weber, Anett; Steiner, Winfried J.; Lang, Stefan: A comparison of semiparametric and heterogeneous store sales models for optimal category pricing (2017)
  10. Anita Thieler; Roland Fried; Jonathan Rathjens: RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression (2016) not zbMATH
  11. Azzimonti, Dario; Bect, Julien; Chevalier, Clément; Ginsbourger, David: Quantifying uncertainties on excursion sets under a Gaussian random field prior (2016)
  12. Christoph Bergmeir and Daniel Molina and José Benítez: Memetic Algorithms with Local Search Chains in R: The Rmalschains Package (2016) not zbMATH
  13. Marie Delignette-Muller; Christophe Dutang: fitdistrplus: An R Package for Fitting Distributions (2015) not zbMATH
  14. Chevalier, Clément; Picheny, Victor; Ginsbourger, David: \textitKrigInv: an efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging (2014)
  15. Kang, Chaeryon; Janes, Holly; Huang, Ying: Combining biomarkers to optimize patient treatment recommendations (2014)
  16. Katharine Mullen: Continuous Global Optimization in R (2014) not zbMATH
  17. Luca Scrucca: GA: A Package for Genetic Algorithms in R (2013) not zbMATH
  18. Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.: Generalized linear latent variable models with flexible distribution of latent variables (2012)
  19. Olivier Roustant; David Ginsbourger; Yves Deville: DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012) not zbMATH
  20. Subramanian, Sundarraman: Model-based likelihood ratio confidence intervals for survival functions (2012)

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