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I am generating random weight as per e.g. below. The I generate a set of 3 values say 100, 250, 300 and I multiple them with the weights below

Initial population.

1. 0.1 0.7 0.2

2. 0.4 0.5 0.1

3. 0.3 0.5 0.2

4. 0.6 0.1 0.3

5  0.3 0.3 0.4

6. 0.2 0.2 0.6

Multiplication of weight

1. 0.1*100 + 0.7*250 + 0.2*300 = 245

2. 0.4*100 + 0.5*250 + 0.1*300 = 195

3. 0.3*100 + 0.5*250 + 0.2*300 = 215

4. 0.6*100 + 0.1*250 + 0.3*300 = 175

5. 0.3*100 + 0.3*250 + 0.4*300 = 225

6. 0.2*100 + 0.2*250 + 0.6*300 = 250

So after the multiplication we selected the individual 6 as the best combination of weights. This method works fine for us but we are trying to link it to any available algorithm. Does this algorithm have a specific name? Any idea or help on this? Is this not a genetic algorithm?

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    $\begingroup$ Hi biz14 and welcome to scicomp! It's a bit difficult to understand what problem you're working on and what your specific question is. Rewriting it with more explicit detail will help us to help you better. $\endgroup$ – Paul Jan 6 '14 at 20:24
  • $\begingroup$ @Paul sorry for my question I have rewrite with clearer steps and problem. Thank you. $\endgroup$ – biz14 Jan 7 '14 at 4:12
  • $\begingroup$ It is clear that your objective is to find the maximum dot product between "populations" and "weights" given a fixed vector of "weights". However, your constraints are not clear. Are you restricting yourself to find a maximum only among the randomly generated "populations"? Or are you really looking for a maximum among all feasible values of "population" triplets? $\endgroup$ – Paul Jan 7 '14 at 18:06
  • $\begingroup$ My constraint is that the weight are calculated using a sum formula from 3 different input which I think pretty much straight forward. Yes my restriction is the finding best among the population of the triplets ? So what is your suggestion? $\endgroup$ – biz14 Jan 8 '14 at 13:58
  • $\begingroup$ My answer below remains the same. $\endgroup$ – Paul Jan 8 '14 at 15:09
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The algorithm you have chosen for your optimization problem sounds like a variant of the family of algorithms known as random optimization.

I'd like to also add that since your objective and constraints seem to both be linear (please correct me if I'm wrong), your problem can be casted as a Linear Programming problem, which is a well studied problem with more efficient algorithms than the one you propose.

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