how to pass initial guess to ga(),this is my sample code and i want to initialize complex initial guess in the code please help

[z, f] = ga(@objfun,2);

ga stopped because the average change in the fitness value is less than options.FunctionTolerance.

disp(z)

    0.4017    0.4718

function f = objfun(X)

% Create complex value from real and imaginary

z = X(:, 1) + i*X(:, 2); 

f = z.^2 - z + 1;

f = abs(f); 
end

Answer 1

[Walter Roberson]

In order to pass in an initial guess, you need to create an options structure and set

InitialPopulationMatrix specifies an initial population for the genetic algorithm. The default value is [], in which case ga uses the default CreationFcn to create an initial population. If you enter a nonempty array in the InitialPopulationMatrix, the array must have no more than PopulationSize rows, and exactly nvars columns, where nvars is the number of variables, the second input to ga or gamultiobj. If you have a partial initial population, meaning fewer than PopulationSize rows, then the genetic algorithm calls CreationFcn to generate the remaining individuals.

[Odette]

initial=[1 2];
nvars=2;
opts=optimoptions('ga','InitialPopulationMatrix',initial);
[z, f] = ga(@objfun,nvars);

ga stopped because it exceeded options.MaxGenerations.

disp(z)

    0.5297   -0.8608

function f = objfun(X)
% Create complex value from real and imaginary
z = X(:, 1) + i*X(:, 2); 
f = z.^2 - z + 1;
f = abs(f); 
end

[Odette]

suppose i want z=1+2*i to be the initial guess..is this correct..im new to ga

[Walter Roberson]

initial=[1 2];
nvars=2;
opts=optimoptions('ga','InitialPopulationMatrix',initial,'PopulationSize',1);
[z, f] = ga(@objfun,nvars,[],[],[],[],[],[],[],opts);

ga stopped because the average change in the fitness value is less than options.FunctionTolerance.

disp(z)

     1     2

function f = objfun(X)
% Create complex value from real and imaginary
z = X(:, 1) + i*X(:, 2); 
f = z.^2 - z + 1;
f = abs(f); 
end

A population as small as 1 is likely to cause problems.

[Odette]

thankyou so much ffor your time