matlab genetic algorithm how to extract the best fitness values and iteration numbers in matlab

To extract the best fitness values and iteration numbers from a MATLAB Genetic Algorithm (GA) optimization run, you can use the output functions provided by the GA toolbox. Output functions allow you to customize the way the GA algorithm operates, including capturing important information during the optimization process.

Here's the general procedure to extract the best fitness values and iteration numbers:

1. Define an output function that will be called at each generation during the optimization process. This function will store the best fitness values and iteration numbers.

main.m
function [state, options, optchanged] = output_func(options, state, flag)
    persistent bestFitnessValues
    persistent iterationNumbers
    
    optchanged = false;

    switch flag
        case 'init'
            bestFitnessValues = [];
            iterationNumbers = [];
        case 'iter'
            bestFitness = min(state.Score);
            bestFitnessValues = [bestFitnessValues; bestFitness];
            iterationNumbers = [iterationNumbers; state.Generation];
        case 'done'
            % Additional processing if required
    end
end
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2. During the optimization setup, specify this output function as 'OutputFcn' in the options of the GA solver.

main.m
options = optimoptions('ga', 'OutputFcn', @output_func);
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3. Run the GA optimization using ga function, passing the fitness function and other necessary inputs as arguments.

main.m
[x, fval, exitflag, output] = ga(@fitness_function, nvars, A, b, Aeq, beq, lb, ub, @nonlcon, options);
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4. After running the GA, you can access the best fitness values and iteration numbers from within the output_func function. These values can be accessed using the bestFitnessValues and iterationNumbers arrays.

This approach allows you to capture the best fitness values and iteration numbers at each generation during the GA optimization run. You can then use these values for further analysis or to plot convergence graphs.

Keep in mind that this is just a basic example, and you may need to modify it based on your specific requirements and problem setup.

tags: matlab, genetic-algorithm, optimization, output-function