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 30 articles )

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  1. Pan, Yinghao; Zhao, Ying-Qi: Improved doubly robust estimation in learning optimal individualized treatment rules (2021)
  2. Shi, Chengchun; Song, Rui; Lu, Wenbin: Concordance and value information criteria for optimal treatment decision (2021)
  3. Xiao, Qian; Mandal, Abhyuday; Lin, C. Devon; Deng, Xinwei: EzGP: easy-to-interpret Gaussian process models for computer experiments with both quantitative and qualitative factors (2021)
  4. Xiao, Qian; Xu, Hongquan: A mapping-based universal kriging model for order-of-addition experiments in drug combination studies (2021)
  5. 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)
  6. Mickaël Binois and Victor Picheny: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis (2019) not zbMATH
  7. Chadsuthi, Sudarat; Wichapeng, Surapa: The modelling of hand, foot, and mouth disease in contaminated environments in Bangkok, Thailand (2018)
  8. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  9. 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)
  10. Zhang, Baqun; Zhang, Min: Variable selection for estimating the optimal treatment regimes in the presence of a large number of covariates (2018)
  11. 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)
  12. 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)
  13. Weber, Anett; Steiner, Winfried J.; Lang, Stefan: A comparison of semiparametric and heterogeneous store sales models for optimal category pricing (2017)
  14. Anita Thieler; Roland Fried; Jonathan Rathjens: RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression (2016) not zbMATH
  15. Azzimonti, Dario; Bect, Julien; Chevalier, Clément; Ginsbourger, David: Quantifying uncertainties on excursion sets under a Gaussian random field prior (2016)
  16. Christoph Bergmeir and Daniel Molina and José Benítez: Memetic Algorithms with Local Search Chains in R: The Rmalschains Package (2016) not zbMATH
  17. Marie Delignette-Muller; Christophe Dutang: fitdistrplus: An R Package for Fitting Distributions (2015) not zbMATH
  18. Chevalier, Clément; Picheny, Victor; Ginsbourger, David: \textitKrigInv: an efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging (2014)
  19. Kang, Chaeryon; Janes, Holly; Huang, Ying: Combining biomarkers to optimize patient treatment recommendations (2014)
  20. Katharine Mullen: Continuous Global Optimization in R (2014) not zbMATH

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