[mailshanx]

I would reserve GAs (at least in their naive form) as a last-resort option. They are extremely inefficient solutions because it doesn't exploit any structure in the problem.

Essentially, a GA solution consists of:

1. Select a random point(s) in the search space, hoping that you have arrived at a sufficiently good solution.

2. If none of the solutions are not good enough, generate a new set of points by combining the best available points and adding a bit of random error.

3. Repeat the above until time has run out/u've hit a good enough solution.

However having said that, GAs can be a good option if 1)not much is known about the function you are trying to optimize, or 2)the crossover/mutation functions are designed to reflect some problem structure, or 3)the search space is small enough.