🚀 go-pugleaf

Ryan D. Alexander <ryanalex@lamar.colostate.edu> wrote in message
news:3800F279.AD543BD3@lamar.colostate.edu...
> In attempting to model chromosomes packed within a nucleus, I've decided
that I
> need a type of Monte Carlo Minimization routine.  Scouring the Web has
turned up
> little in the way progress, so I've turned to you all in NG-Land...
>

 An interesting alternative to the suggestions already made is the approach
of the algorithm by A.Zilinskas (published as "Optimization of
one-dimensional multimodal functions", Applied Statistics 1978, v27,
p367-375 (Algorithm AS133)). This gives Fortran code, which should also be
available from statlib under directory apstat. This approach seems only
suited to 1-D problems because of the number of function evaluations. It has
the effect of balancing doing extra function evauations in regions where the
function is "roughest", with evaluations in the neighbourhoods of possibly
competing local optima. The locations where the function is evaluated are
decided so as to supply the best improvement of information about the
location of the overall optimum in a statistical sense.

However, the "genetic" algorithms already mentioned seem well suited to the
problem.