how to plot fitness function changes over each generation in genetic algorithm in matlab

To plot the changes in the fitness function over each generation in a genetic algorithm in MATLAB, you can follow these steps:

1. Perform the genetic algorithm and collect the best fitness value for each generation.
2. Create a vector or array to store the best fitness values.
3. Inside the genetic algorithm loop, update the fitness values vector with the best fitness value for that generation.
4. After the genetic algorithm finishes, use the plot function to visualize the changes in the fitness function.

Here is an example code snippet to illustrate the process:

main.m
fitness_values = [];  % Storing best fitness values for each generation

% your genetic algorithm code...
for generation = 1:num_generations
    % Evaluate fitness values for population
    
    % Update fitness_values vector with the best fitness value
    best_fitness = max(fitness_values_of_current_generation);
    fitness_values = [fitness_values best_fitness];
    
    % ... rest of the genetic algorithm steps ...
end

% Plot the changes in the fitness function
generation_numbers = 1:num_generations;
plot(generation_numbers, fitness_values);
xlabel('Generation');
ylabel('Fitness Value');
title('Fitness Function Changes over Generations');
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In this example, you need to customize the genetic algorithm code based on your specific problem.

Make sure to replace fitness_values_of_current_generation with the actual fitness values evaluated for each individual in the current generation.

Also, adjust num_generations to the total number of generations you want to run in your genetic algorithm.

This code will create a plot showing the changes in the fitness function over generations, with the generation number on the x-axis and the fitness value on the y-axis. You can customize the plot appearance as desired using additional plot options.

Remember to replace the problem-specific code within the genetic algorithm loop with your own problem's evaluation and selection mechanisms.