This webpage is for the distribution of the R version of GENOUD
(GENetic Optimized Using Derivatives). Genoud is a 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 a statistical model's estimating
function (for example, a log-likelihood) is nonlinear in the model's
parameters, the function to be optimized 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. Genoud,
via the cluster option, supports the use of multiple
computers, CPUs or cores to perform parallel computations.