Global optimization using genetic algorithm
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I use ff2n function to generate the input for my function. For the example of ff2n(4), some sample input rows are:
1 0 1 0
1 1 0 0
0 0 1 1
...
...
Based on the input from ff2n(4), a total of 16 scalar values will be generated for my function. If I sorted the 16 scalar values generated, I will be able to find the minimum value.
Can I use GA to find the minimum value for my function based on the following commands?
>> [x fval exitflag] = ga(@my_fun, 2)
My input values are fixed, which are 0 and 1. Can I consider the total number of variables as 2? Is it possible for me to get the following sample output:
x = 1 1 0 0
fval = 0.12345
exitflag = 1
If this GA command is inapplicable, please give me some suggestions how can I use GA to solve my problem.
Alternatively, in order to get the x and fval values, can I use the optimization toolbox by choosing GA solver, enter my fitness function, put 2 as number of variables and run it?
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Answers (1)
Alan Weiss
on 23 Oct 2012
You might do well to read the documentation on ga with integer constraints. If I understand you, for this case you have n = 4 dimensions, not 2, because there are 4 design variables. Each design variable is binary, so is integer valued with a lower bound of 0 and an upper bound of 1.
Alan Weiss
MATLAB mathematical toolbox documentation
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