How to output additional variables from objective function using ga optimization?

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I am having trouble to output variables from the objective function using ga other than the value of the objective funtion.
Does anyone have an idea on how to do that?

Answers (3)

Alan Weiss
Alan Weiss on 10 Apr 2023
I am not sure what you are trying to do. You might be trying to pass intermediate values calculated within ga to reuse, for example in a constraint function. I am sorry, but that generally does not work for ga, as the order of operations that ga internally uses does not lend itself to that purpose. In other words, the technique in Objective and Nonlinear Constraints in the Same Function does not work for ga.
If you are trying to do something else, ask again with more detail.
Alan Weiss
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Moritz Wegner
Moritz Wegner on 10 Apr 2023
thank you for your reply.
I am using ga for the optimization of an electric motor. I am using ga to generate random motor configurations. The objectiv function then calls a finite element programm which returns the mean torque and other parameters which are then used to calculate the value of the fitness function to evaluate the randomly generated motor configuration. I am using an output function which saves the fitness function value of the best individual of each generation and the individual. I am trying to store the parameters such as torque as well. And I didnt find a way to return those parameters from the objective function to pass them to the output function. Is there a way?

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Star Strider
Star Strider on 10 Apr 2023
Does anyone have an idea on how to do that?
After the optimisation terminates, run the objective function with the optimised parameter values and return all the desired outputs.
If the fitness function returns several results, for example:
function [a,b,c] = ftnsfcn(a,x)
... CODE ...
and the ga call is something like this:
[A,fval] = ga(@(a)ftnsfcn(a,x), NrParms);
after the optimiization terminates, run this:
[a,b,c] = ftnsfcn(A,x)
This would be easier to illustrate with a more specific example if you have one to share.
Star Strider
Star Strider on 10 Apr 2023
I’m slightly lost at this point. You can save the best individual in each generation, and then afterwards use that information as input to the fitness function and get all the outputs from it for each saved individual.
That’s what I would do, anyway.

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Walter Roberson
Walter Roberson on 10 Apr 2023
Have your objective function return a data structure of additional values. memoize() your objective function with a fair size buffer. Have the output function (which runs once per generation) fetch the best value of the model parameters and call the memorized function to retrieve the associated data structure, and store the values somewhere.
Walter Roberson
Walter Roberson on 10 Apr 2023
memoized_objective = memoize(@objective);
memoized_objective.Cachesize = 1000;
options = optimoptions('ga','outputfcn', @(options,state,flag)save_best_data(options,state,flag,memoized_objective));
[bestx, fval, exitflag] = ga(memoized_objective, nvars, A, b, Aeq, beq, lb, ub, nonlcon, options);
best_data = recall_best_data();
function [cost, data_to_save] = objective(x)
data_to_save = {torque, other_parameters};
cost = whatever;
function initialize_saved_data()
do whatever is needed to initialize the store of saved data
function [state,options,optchanged] = save_best_data(options,state,flag,memoized_objective)
if state.LastImprovement == state.Generation
scores = state.Score;
[~, minidx] = min(scores);
best_params = state.Population(minidx,:);
[best_cost, data_to_save] = memoized_objective(best_params);
now save data_to_save however is appropriate
function all_saved_data = recall_best_data()
now recall the saved data and assign it to all_saved_data

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