I'm applying a simple genetic algorithm to an optimization problem (I need to find a 4-parameter function's global minimum)
I start the generations run with say
100 chromosomes which I then evaluate, select, breed (crossover + mutate) and repeat for
When the algorithm is done (the
N_gen generations were processed) I'm left with the best fitted solution: the one which evaluates to the minimum value from the last generation's
But this also leaves me with
99 other well fitted solutions that I have a priori no use for.
Is there a way to extract some information from these other solutions? Perhaps an uncertainty estimate for the best fitted solution?
Intuitively I'd calculate the standard deviation for the parameters values obtained from the last generation and use that as an uncertainty estimate for the best fitted solution. Kind of like using those other solutions as a bootstrap for my best solution.