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Hello! I have a fitness functiion:
function z = my_fun(x)
Skorost8.input(1,1).mf(1,1).params=[x(1),x(2),x(3)];
Skorost8.input(1,1).mf(1,2).params=[x(4),x(5),x(6)];
Skorost8.input(1,1).mf(1,3).params=[x(7),x(8),x(9)];
Skorost8.input(1,1).mf(1,4).params=[x(10),x(11),x(12)];
Skorost8.input(1,2).mf(1,1).params=[x(13),x(14),x(15)];
Skorost8.input(1,2).mf(1,2).params=[x(16),x(17),x(18)];
Skorost8.input(1,2).mf(1,3).params=[x(19),x(20),x(21)];
load_system('petina.mdl');
sim('petina.mdl');
close_system('petina.mdl');
z=simout.signals.values;
And petina.mdl file, which it use. The mdl-file write to workspace a massive of values, which must be a minimal (z=simout.signals.values;)
When I try to use it with gatool I get:
>> x=ga(@my_fun,21);
??? Subscripted assignment dimension mismatch.
Error in ==> fcnvectorizer at 14
y(i,:) = feval(fun,(pop(i,:)));
Error in ==> makeState at 47
Score =
fcnvectorizer(state.Population(initScoreProvided+1:end,:),FitnessFcn,1,options.SerialUserFcn);
Error in ==> gaunc at 41
state = makeState(GenomeLength,FitnessFcn,Iterate,output.problemtype,options);
Error in ==> ga at 279
[x,fval,exitFlag,output,population,scores] = gaunc(FitnessFcn,nvars, ...
Caused by:
Failure in user-supplied fitness function evaluation. GA cannot continue.
How to use correctly gatool in this case? thank you very much!
0 comentarios
Respuesta aceptada
Sean de Wolski
el 5 de En. de 2012
dbstop if error
Which will stop deep inside of ga(). Now put a break point in first line of your objective function. Run the feval line that causes the error and step through your fitness function to find the error and analyze the inputs.
5 comentarios
Sean de Wolski
el 5 de En. de 2012
Yes, a 1x450 would be 450 objectives (which sounds intuitively like a lot (way too many) to me!). Especially since you only want to optimize 21 variables.
If you look in the doc for ga() on line one it explains the output:
"x = ga(fitnessfcn,nvars) finds a local unconstrained minimum, x, to the objective function, fitnessfcn. nvars is the dimension (number of design variables) of fitnessfcn. The objective function, fitnessfcn, accepts a vector x of size 1-by-nvars, and returns a scalar evaluated at x."
Could you explain what you're trying to optimize?
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