Simulated Annealing
From Wikipeda
s ← s0; e ← E(s)                             // Initial state, energy.
sb ← s; eb ← e                               // Initial "best" solution
k ← 0                                        // Energy evaluation count.
while k < kmax and e > emax                  // While time remains & not good enough:
  sn ← neighbour(s)                          //   Pick some neighbour.
  en ← E(sn)                                 //   Compute its energy.
  if en < eb then                            //   Is this a new best?
    sb ← sn; eb ← en                         //     Yes, save it.
  if P(e, en, temp(k/kmax)) > random() then  //   Should we move to it?
    s ← sn; e ← en                           //     Yes, change state.
  k ← k + 1                                  //   One more evaluation done
return sb                                    // Return the best solution found.

Keys
For att varialbels find the most important one and its range. Lock that 
variable and then start look for the next.

Runs the simulator many times. Sorts the scores and the treatments in 10%best and
90% rest. For for each variable sort the values into a number of bins according. One
set of bins for best and one for the rest. Form this caluclate the likelhood and the 
get the scoringfunction 13 (adjust liklehood with the support). Fix the highest 
scoring vaiable int the range for the most frequent bin. Repete this iterativly for
all the variables.