Binary GA returns floating point numbers

Question

Fabian Hofmann 2024-2-19

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2084048-binary-ga-returns-floating-point-numbers

评论： Fabian Hofmann 2024-2-19

Hello,

I am trying to find optimal wavelengths in NIR spectra to perform PLS regression. I have working code but the solution includes sometimes floating point numbers. My question is now how to tell ga that only 0 and 1 are possible gene values.

Or can I simply say that any non zero value is true?

% Set seed for reproducability
rng(42);
% Download data and define arrays
url = 'https://figshare.com/ndownloader/files/1649903';
filename = 'data.xlsx';
data = readmatrix(websave(filename, url));
X = data(2:end,9:end);
y = data(2:end,1);
% Average blocks of 4 wavelengths
Xavg = mean(reshape(X, [size(X,1), size(X,2)/4, 4]), 3);
% Define the fitness function
fitness_function = @(solution) 1.0 / sqrt(mean((y - regressor(Xavg(:,logical(solution)), y)).^2));
% Define the initial population
init_pop = generate_initial_population(size(Xavg,2), 50, 25);
% Define the GA instance
options = optimoptions('ga', 'PopulationSize', 50, 'InitialPopulationMatrix', init_pop, ...
    'MutationFcn', {@mutationadaptfeasible, 0.3}, 'CrossoverFcn', @crossoverscattered, ...
    'EliteCount', 10, 'MaxGenerations', 100, 'UseParallel', true, 'Display', 'iter');
% Run GA
[solution, fval] = ga(fitness_function, size(Xavg,2), [], [], [], [], zeros(size(Xavg,2),1), ones(size(Xavg,2),1), [], options);
function y_pred = regressor(X, y)
    % Specify parameter space 
    parameters_gs = 1:6;
    best_mse = inf;
    best_n_components = 0;
    for n_components = parameters_gs
        % Define PLSRegression object
        [~,~,~,~,beta] = plsregress(X, y, n_components);
        % Fit to data
        y_pred = [ones(size(X,1),1) X] * beta;
        % Calculate a final y with best choice of parameters
        mse = mean((y - y_pred).^2);
        if mse < best_mse
            best_mse = mse;
            best_n_components = n_components;
        end
    end
    [~,~,~,~,beta] = plsregress(X, y, best_n_components);
    y_pred = [ones(size(X,1),1) X] * beta;
end
function init_population = generate_initial_population(array_size, solutions_per_pop, number_of_bands)
    % Starts with a boolean array of zeroes
    init_population = false(solutions_per_pop, array_size);
    % Define an index array the size of the spectral wavelengths
    index_array = 1:array_size;
    for i = 1:solutions_per_pop
        % Randomly shuffle the array in place
        index_array = index_array(randperm(length(index_array)));
        % Select the first number_of_bands of the shuffled array and use it to flip the population array
        init_population(i, index_array(1:number_of_bands)) = ~init_population(i, index_array(1:number_of_bands));
    end
    init_population = double(init_population);
end

Thanks for helping

F

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Answer 1

Walter Roberson 2024-2-19

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2084048-binary-ga-returns-floating-point-numbers#answer_1412003

在 MATLAB Online 中打开

% Set seed for reproducability
rng(42);
% Load data and define arrays
data = readmatrix('Data/File_S1.xlsx');
Error using readmatrix
Unable to find or open 'Data/File_S1.xlsx'. Check the path and filename or file permissions.
X = data(2:end,9:end);
y = data(2:end,1);
% Average blocks of 4 wavelengths
Xavg = mean(reshape(X, [size(X,1), size(X,2)/4, 4]), 3);
% Define the fitness function
fitness_function = @(solution) 1.0 / sqrt(mean((y - cv_regressor(Xavg(:,logical(solution)), y)).^2));
% Define the initial population
init_pop = generate_initial_population(size(Xavg,2), 50, 25);
% Define the GA instance
options = optimoptions('ga', 'PopulationSize', 50, 'InitialPopulationMatrix', init_pop, ...
    'MutationFcn', {@mutationadaptfeasible, 0.3}, 'CrossoverFcn', @crossoverscattered, ...
    'EliteCount', 10, 'MaxGenerations', 100, 'UseParallel', true, 'Display', 'iter', ...
    'PopulationType', 'bitstring');
% Run GA
[solution, fval] = ga(fitness_function, size(Xavg,2), [], [], [], [], zeros(size(Xavg,2),1), ones(size(Xavg,2),1), [], options);
function y_pred = regressor(X, y)
    % Specify parameter space 
    parameters_gs = 1:6;
    best_mse = inf;
    best_n_components = 0;
    for n_components = parameters_gs
        % Define PLSRegression object
        [~,~,~,~,beta] = plsregress(X, y, n_components);
        % Fit to data
        y_pred = [ones(size(X,1),1) X] * beta;
        % Calculate a final y with best choice of parameters
        mse = mean((y - y_pred).^2);
        if mse < best_mse
            best_mse = mse;
            best_n_components = n_components;
        end
    end
    [~,~,~,~,beta] = plsregress(X, y, best_n_components);
    y_pred = [ones(size(X,1),1) X] * beta;
end
function init_population = generate_initial_population(array_size, solutions_per_pop, number_of_bands)
    % Starts with a boolean array of zeroes
    init_population = false(solutions_per_pop, array_size);
    % Define an index array the size of the spectral wavelengths
    index_array = 1:array_size;
    for i = 1:solutions_per_pop
        % Randomly shuffle the array in place
        index_array = index_array(randperm(length(index_array)));
        % Select the first number_of_bands of the shuffled array and use it to flip the population array
        init_population(i, index_array(1:number_of_bands)) = ~init_population(i, index_array(1:number_of_bands));
    end
    init_population = double(init_population);
end