best fitness function values at each generation in ga optimization figure in matlab

To plot the best fitness function values at each generation in a Genetic Algorithm (GA) optimization in MATLAB, you can follow the steps below:

Define your fitness function that evaluates the performance of each individual in the population.
Set up the GA parameters such as the number of generations, population size, crossover and mutation rates, etc.
Run the GA optimization using the ga function in MATLAB, and store the best fitness values at each generation.

Here is an example code snippet that demonstrates this process:


main.m
% Fitness function (example)
fitnessFunction = @(x) x^2;

% GA parameters
numGenerations = 100; % Number of generations
populationSize = 50; % Population size
crossoverRate = 0.8; % Crossover rate
mutationRate = 0.02; % Mutation rate

% Store best fitness values
bestFitness = zeros(numGenerations, 1);

% Run GA optimization
for generation = 1:numGenerations
    % Run GA for one generation
    [population, ~, ~, ~] = ga(fitnessFunction, 1, [], [], [], [], [], [], [], generation);
    
    % Evaluate fitness values
    fitnessValues = arrayfun(fitnessFunction, population);
    
    % Find the best fitness value in the current generation
    [bestFitnessValue, bestIndividualIndex] = min(fitnessValues);
    
    % Store the best fitness value
    bestFitness(generation) = bestFitnessValue;
    
    % Print out the generation and the best fitness value
    fprintf('Generation %d: Best Fitness = %f\n', generation, bestFitnessValue);
end

% Plot the best fitness values at each generation
figure;
plot(1:numGenerations, bestFitness);
xlabel('Generation');
ylabel('Best Fitness Value');
title('Convergence of the GA Optimization');

% Show the final best fitness value
finalBestFitness = bestFitness(end);
fprintf('Final Best Fitness Value: %f\n', finalBestFitness);
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In this example, the fitness function is defined as a simple quadratic function x^2. You can replace it with your own fitness function.

The code runs the GA optimization for the specified number of generations and stores the best fitness value at each generation in the bestFitness array. It then plots the best fitness values using the plot function.

Make sure to replace the fitness function with your own function and adjust the GA parameters according to your problem.

I hope this helps! Let me know if you have any further questions.