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when using genetic algorithm, the number of variables(nvar) is dependant on the row vector(x) that my fitness function accepts. How can I deal with that?

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Shadan on 29 Jul 2013
Answered: amanita on 29 Nov 2013
Hello all,
I need to write something like this: ga(h, sum(x(1:5)),[],[],[],[],LB,UB,[],[],ga_opts);
as you see, the number of variables (nvar) is dependant on the vector I want to optimize(x). Is there any way I can deal with this problem?
Thank you

Answers (2)

Evan on 29 Jul 2013
Edited: Evan on 29 Jul 2013
Have you looked into the varargin function?
help varargin
You could then parse the arguments contained within varargin based upon whatever conditions of your row vector x that determine their order/number.
  1 Comment
Shadan on 29 Jul 2013
Thank you for your response. The thing is that varargin will not be recognized outside of the function definition. for example if I write: ga(h, sum(varargin{1}(1:5)),[],[],[],[],LB,UB,[],[1:5],ga_opts);

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amanita on 29 Nov 2013
I dont know if this is relevant, but i usually set nvars as the maximum number of variables and keep in the fitness function only the ones needed. For example, i have a vector of coefficients W that i want to optimize, but its length is dependent on an integer variable I, ie; If i have I=2 i need a vector W with 2 elements, if I=4 i need W with 4 elements. If the maximum number for I is 10. Then:
h = @(X) NETWORK_mex(X);
LB=[1 -1*ones(1,10)]
UB=[10 ones(1,10)]
[x, err] = ga(h, nvars,[],[],[],[],LB,UB,[],1,ga_opts);
And inside the fitness function:
function J=NETWORK(X)

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