Genetic/Evolutionary algorithms and local minima/maxima
evolutionary-algorithm
Solution
Simulated annealing is a probabilistic optimization technique -- it's not supposed to give you more precise answers, it's supposed to give you approximations faster.
Problem
I have run across several posts and articles that suggests using things like simulated annealing to avoid the local minima/maxima problem. I don't understand why this would be necessary if you started out with a sufficiently large random population. Is it just another check to insure that the initial population was, in fact, sufficiently large and random? Or are those techniques just an alternative to producing a "good" initial population?