Does anyone know how I can pass variables back from a
genetic algorithm (GA) optimisation function to the m file
that initiated the GA?
I am using anonymous functions to pass variables to the GA,
but cannot work out how to get variables back from the GA.
It appears that the only allowable outputs are
One way could be to write each variable to file and then
read them back from that file, but that is very
inefficient. I am trying to avoid using global variables as
I run into problems with these when running the GA in
parallel using the parallel computing toolbox.
Hopefully someone can point me in the right direction.
"Dave Brackett" <email@example.com> wrote in message
> Does anyone know how I can pass variables back from a
> genetic algorithm (GA) optimisation function to the m
> that initiated the GA?
> I am using anonymous functions to pass variables to the
> but cannot work out how to get variables back from the
> It appears that the only allowable outputs are
> One way could be to write each variable to file and then
> read them back from that file, but that is very
> inefficient. I am trying to avoid using global variables
> I run into problems with these when running the GA in
> parallel using the parallel computing toolbox.
> Hopefully someone can point me in the right direction.
I have found a workaround for this by recalculating based
on the GA output x. This only adds minor overhead as it is
recalculated only once at the very end of the optimisation
run. This then eliminates the need to pass any other
outputs from the GA to the initiating function.
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