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Genetic Optimization Using Derivatives: The rgenoud Package for R


  • Mebane Jr., Walter R.
  • Sekhon, Jasjeet S.


genoud is an R function that combines evolutionary algorithm methods with a derivative-based (quasi-Newton) method to solve difficult optimization problems. genoud may also be used for optimization problems for which derivatives do not exist. genoud solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized. When the function to be optimized (for example, a log-likelihood) is nonlinear in the model's parameters, the function will generally not be globally concave and may have irregularities such as saddlepoints or discontinuities. Optimization methods that rely on derivatives of the objective function may be unable to find any optimum at all. Multiple local optima may exist, so that there is no guarantee that a derivative-based method will converge to the global optimum. On the other hand, algorithms that do not use derivative information (such as pure genetic algorithms) are for many problems needlessly poor at local hill climbing. Most statistical problems are regular in a neighborhood of the solution. Therefore, for some portion of the search space, derivative information is useful. The function supports parallel processing on multiple CPUs on a single machine or a cluster of computers.

Suggested Citation

  • Mebane Jr., Walter R. & Sekhon, Jasjeet S., 2011. "Genetic Optimization Using Derivatives: The rgenoud Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i11).
  • Handle: RePEc:jss:jstsof:v:042:i11

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    References listed on IDEAS

    1. Alberto Abadie & Javier Gardeazabal, 2001. "The Economic Costs of Conflict: A Case-Control Study for the Basque Country," NBER Working Papers 8478, National Bureau of Economic Research, Inc.
    2. Braumoeller, Bear F., 2003. "Causal Complexity and the Study of Politics," Political Analysis, Cambridge University Press, vol. 11(03), pages 209-233, June.
    3. Wand, Jonathan & King, Gary & Lau, Olivia, 2011. "anchors: Software for Anchoring Vignette Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i03).
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    Cited by:

    1. Isabelle Salle & Murat Yıldızoğlu, 2014. "Efficient Sampling and Meta-Modeling for Computational Economic Models," Computational Economics, Springer;Society for Computational Economics, vol. 44(4), pages 507-536, December.
    2. repec:spr:waterr:v:31:y:2017:i:9:d:10.1007_s11269-017-1660-3 is not listed on IDEAS
    3. Isabelle SALLE & Marc-Alexandre SENEGAS & Murat YILDIZOGLU, 2013. "How Transparent About Its Inflation Target Should a Central Bank be? An Agent-Based Model Assessment," Cahiers du GREThA 2013-24, Groupe de Recherche en Economie Théorique et Appliquée.
    4. Chevalier, Clément & Picheny, Victor & Ginsbourger, David, 2014. "KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1021-1034.
    5. Muñoz-Mas, Rafael & Vezza, Paolo & Alcaraz-Hernández, Juan Diego & Martínez-Capel, Francisco, 2016. "Risk of invasion predicted with support vector machines: A case study on northern pike (Esox Lucius, L.) and bleak (Alburnus alburnus, L.)," Ecological Modelling, Elsevier, vol. 342(C), pages 123-134.
    6. Chaeryon Kang & Holly Janes & Ying Huang, 2014. "Combining biomarkers to optimize patient treatment recommendations," Biometrics, The International Biometric Society, vol. 70(3), pages 695-707, September.
    7. Elio Marchione & Shane D Johnson & Alan Wilson, 2014. "Modelling Maritime Piracy: A Spatial Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(2), pages 1-9.
    8. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2012. "A Robust Method for Estimating Optimal Treatment Regimes," Biometrics, The International Biometric Society, vol. 68(4), pages 1010-1018, December.
    9. Henningsen, Arne & Mpeta, Daniel F. & Adem, Anwar S. & Kuzilwa, Joseph A. & Czekaj, Tomasz G., 2015. "The Effects of Contract Farming on Efficiency and Productivity of Small-Scare Sunflower Farmers in Tanzania," 2015 Conference, August 9-14, 2015, Milan, Italy 212478, International Association of Agricultural Economists.
    10. Alarcón, Silverio & Sánchez, Mercedes, 2016. "Is there a virtuous circle relationship between innovation activities and exports? A comparison of food and agricultural firms," Food Policy, Elsevier, vol. 61(C), pages 70-79.
    11. Henningsen, Arne & Mpeta, Daniel F. & Adem, Anwar S. & Kuzilwa, Joseph A. & Czekaj, Tomasz G., 2015. "A Meta-Frontier Approach for Causal Inference in Productivity Analysis: The Effect of Contract Farming on Sunflower Productivity in Tanzania," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206200, Agricultural and Applied Economics Association.
    12. repec:eee:ejores:v:266:y:2018:i:1:p:179-192 is not listed on IDEAS
    13. Ummel, Kevin & Fant, Charles, 2014. "Identifying cost-effective deployment strategies through spatiotemporal modelling," WIDER Working Paper Series 121, World Institute for Development Economic Research (UNU-WIDER).
    14. repec:bla:jorssb:v:79:y:2017:i:4:p:1165-1185 is not listed on IDEAS
    15. Anett Weber & Winfried J. Steiner & Stefan Lang, 2017. "A comparison of semiparametric and heterogeneous store sales models for optimal category pricing," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 403-445, March.

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