MATLAB: Genetic algorithm for curve fitting

Question

Hello Everyone

I want to Fit a curve (called MFit) on another curve (called M)

MFit is a function and defined by the following relation:

MFit = M0 + c0 * (h * z – log(z – z0 / z0))

and M is a 100-element vector. I want to fit MFit on M by choosing the right value of c0. z is a 100-element vector and M0,h and z0 are constants. What I have in mind is to define a target function as

Fun1 = abs(M – MFit)

so that by minimzing it, MFit will be fit. This is my proposed method:

MFit = @(c0) (M0 + c0 * (z – h * log((z + z0) / z0)));

Fun1 = @(c0) abs(M – MFit);

rng default

C0 = ga(Fun1,1);

but things go wrong when I run the code. Can anybody help me how I may solve this problem with genetic algorithm?

Answer 1

I would do something like this (with ‘M’ and the constants already existing in your workspace):

MFit = @(c0,M0,h,z,z0) (M0 + c0 * (z - h * log((z + z0) / z0)));
Fun1 = @(c0) norm(M - MFit(c0,M0,h,z,z0));
c0_est = ga(Fun1, 1);

The fitness funciton must return a scalar value. (The ga call can be further optimised by using an optons structure.)

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