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Genetic Optimization Using Derivatives: The rgenoud Package for R

Listed author(s):
  • Mebane Jr., Walter R.
  • Sekhon, Jasjeet S.
Registered author(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.

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    Article provided by Foundation for Open Access Statistics in its journal Journal of Statistical Software.

    Volume (Year): 042 (2011)
    Issue (Month): i11 ()
    Pages:

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    Handle: RePEc:jss:jstsof:v:042:i11
    DOI: http://hdl.handle.net/10.18637/jss.v042.i11
    Contact details of provider: Web page: http://www.jstatsoft.org/

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    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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