GA: Genetic Algorithms. An R package for optimization using genetic algorithms. The package provides a flexible general-purpose set of tools for implementing genetic algorithms search in both the continuous and discrete case, whether constrained or not. Users can easily define their own objective function depending on the problem at hand. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. GAs can be run sequentially or in parallel.

References in zbMATH (referenced in 29 articles , 1 standard article )

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  1. Kılıç, Muhammet Burak; Şahin, Yusuf; Koca, Melih Burak: Genetic algorithm approach with an adaptive search space based on EM algorithm in two-component mixture Weibull parameter estimation (2021)
  2. Wei, Baolei; Xie, Naiming: Parameter estimation for grey system models: a nonlinear least squares perspective (2021)
  3. Berk, Richard A.: Statistical learning from a regression perspective (2020)
  4. Castellares, Fredy; Patrício, Silvio C.; Lemonte, Artur J.: On gamma-Gompertz life expectancy (2020)
  5. Castellares, Fredy; Patrício, Silvio C.; Lemonte, Artur J.; Queiroz, Bernardo L.: On closed-form expressions to Gompertz-Makeham life expectancy (2020)
  6. Wu, Ho-Hsiang; Ferreira, Marco A. R.; Elkhouly, Mohamed; Ji, Tieming: Hyper nonlocal priors for variable selection in generalized linear models (2020)
  7. Zabinsky, Zelda B.; Dulyakupt, Pattamon; Zangeneh-Khamooshi, Shabnam; Xiao, Cao; Zhang, Pengbo; Kiatsupaibul, Seksan; Heim, Joseph A.: Optimal collection of medical specimens and delivery to central laboratory (2020)
  8. Dufour, Jean-Marie; Neves, Julien: Finite-sample inference and nonstandard asymptotics with Monte Carlo tests and \textsfR (2019)
  9. Fop, Michael; Murphy, Thomas Brendan; Scrucca, Luca: Model-based clustering with sparse covariance matrices (2019)
  10. Mori, U.; Mendiburu, A.; Miranda, I. M.; Lozano, J. A.: Early classification of time series using multi-objective optimization techniques (2019)
  11. Tekeli, Erkut; Kaçıranlar, Selahattin; Özbay, Nimet: Optimal determination of the parameters of some biased estimators using genetic algorithm (2019)
  12. Welchowski, Thomas; Schmid, Matthias: Sparse kernel deep stacking networks (2019)
  13. Chiang, Jyun-You; Zhu, Jianping; Lin, Yu-Jau; Lio, Y. L.; Tsai, Tzong-Ru: Inference from two-variable degradation data using genetic algorithm and Markov chain Monte Carlo methods (2018)
  14. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  15. Sanchez, Fabio; Barboza, Luis A.; Burton, David; Cintrón-Arias, Ariel: Comparative analysis of dengue versus chikungunya outbreaks in Costa Rica (2018)
  16. Shi, Xingjie; Huang, Yuan; Huang, Jian; Ma, Shuangge: A forward and backward stagewise algorithm for nonconvex loss functions with adaptive Lasso (2018)
  17. 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)
  18. Arslan, Güvenç; Oncel, Sevgi Yurt: Parameter estimation of some Kumaraswamy-G type distributions (2017)
  19. Cao, Yongtao; Smucker, Byran J.; Robinson, Timothy J.: A hybrid elitist Pareto-based coordinate exchange algorithm for constructing multi-criteria optimal experimental designs (2017)
  20. Ma, Shaohui; Fildes, Robert: A retail store SKU promotions optimization model for category multi-period profit maximization (2017)

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