From: pdenboef@hotmail.com   
      
   I am using a RCGA with a HUGE search space. I need to extract a set of rules   
   (say 15-30 rules) from very complex data containing 24 attributes. The   
   complex data is gathered from a well-trained ANN, which I wish to use in   
   order to study knowledge representation/ rule extraction.   
      
   Each antecedent has roughly 10 possibilities, which was reduced using an   
   attribute selection and discretization algorithm--Chi2. Classification   
   clustering has reduced the two dimensional classification space to 13   
   possibilities. Clearly my search space is still very big! I have had some   
   success with population sizes of 5000.... but I tend to get stuck in local   
   minimums a lot. Computational cost is huge! I have been looking into   
   optimizing my population size and mutation rate. I have been playing with   
   pop sizes of 1000-5000 and mutation rates of 0.01. My mutation operator   
   randomly changes only one gene.   
      
   Right now I don't know what I should do.....experiment with larger mutation   
   rates to keep diversity and prevent premature convergence into local   
   minimums..... I have heard that small populations with larger mutation rates   
   can be more efficient than larger populations with smaller mutation rates.   
   Shit- I worry that my endeavor is not feasible and that my search space is   
   too damn big.   
      
   Please help!!!!!!!   
      
   the 'dude'   
      
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