[MATLAB] Algoritma Genetika #3 - Teknik Seleksi

Introduction

This tutorial will guide you through the selection techniques used in genetic algorithms, specifically within the MATLAB environment. Understanding these techniques is crucial for optimizing solutions in various computational problems, from engineering to data science. This step-by-step guide will help you implement selection methods effectively.

Step 1: Understanding Selection Techniques

Before diving into coding, familiarize yourself with the primary selection techniques used in genetic algorithms:

• Roulette Wheel Selection: This method selects individuals based on their fitness proportion. The higher the fitness, the higher the chance of being selected.
• Tournament Selection: This method involves selecting a group of individuals randomly and choosing the best among them.
• Rank Selection: Individuals are ranked based on fitness, and selection is done based on this ranking rather than raw fitness values.

Practical Tip

Choose a selection method based on your specific problem's requirements. For example, if diversity is crucial, consider tournament selection.

Step 2: Setting Up MATLAB

Ensure you have MATLAB installed on your computer. If you are new to MATLAB programming, you might want to explore introductory resources available in the "Kelas Terbuka" playlist.

Practical Advice

• Download the source code provided in the video description from GitHub.
• Familiarize yourself with MATLAB's interface and basic commands to ease the programming process.

Step 3: Implementing Roulette Wheel Selection

1. Calculate Total Fitness: Sum the fitness values of all individuals in your population.
2. Calculate Selection Probabilities: For each individual, divide its fitness by the total fitness to get a probability.
3. Select Individuals: Generate random numbers to pick individuals according to their selection probabilities.

Example Code

function selected = roulette_wheel_selection(population, fitness)
    total_fitness = sum(fitness);
    probabilities = fitness / total_fitness;
    cumulative_probabilities = cumsum(probabilities);
    
    % Random selection
    random_numbers = rand(length(population), 1);
    selected = population(arrayfun(@(x) find(cumulative_probabilities >= x, 1), random_numbers));
end

Common Pitfall

Ensure that your fitness values are normalized. If your fitness values vary significantly, it may skew the selection process.

Step 4: Implementing Tournament Selection

1. Choose Tournament Size: Decide the number of individuals to include in the tournament.
2. Select Random Individuals: Randomly choose individuals from the population.
3. Determine the Best: Evaluate the fitness of the selected individuals and choose the best one.

Example Code

function selected = tournament_selection(population, fitness, tournament_size)
    selected = zeros(size(population));
    
    for i = 1:length(population)
        competitors = randi(length(population), [1, tournament_size]);
        [~, best_index] = max(fitness(competitors));
        selected(i) = population(competitors(best_index));
    end
end

Step 5: Implementing Rank Selection

1. Rank Individuals: Arrange the population based on their fitness values.
2. Assign Selection Probabilities: Allocate probabilities based on rankings.
3. Select Individuals: Use a method similar to roulette wheel selection with the new probabilities.

Example Code

function selected = rank_selection(population, fitness)
    [~, ranks] = sort(fitness, 'descend');
    probabilities = (length(population) - ranks + 1) / sum(1:length(population));
    
    cumulative_probabilities = cumsum(probabilities);
    random_numbers = rand(length(population), 1);
    selected = population(arrayfun(@(x) find(cumulative_probabilities >= x, 1), random_numbers));
end

Conclusion

In this tutorial, you have learned about different selection techniques for genetic algorithms and how to implement them in MATLAB. By understanding and applying these methods, you can enhance the efficiency of your genetic algorithm solutions. Next steps include experimenting with these techniques on different datasets or integrating them into larger genetic algorithm frameworks. Happy coding!