[MATLAB] Algoritma Genetika #0 - Pendahuluan tentang apa itu GA

Introduction

This tutorial provides an introduction to Genetic Algorithms (GA) using MATLAB, perfect for beginners looking to understand the fundamental concepts of GA and how to implement them. Whether you are new to programming or want to enhance your MATLAB skills, this guide will walk you through the essential steps.

Step 1: Understanding Genetic Algorithms

• Genetic Algorithms are search heuristics used to solve optimization problems by mimicking natural selection.
• They work by evolving a population of candidate solutions over multiple generations.
• Key concepts include:
  • Population: A set of potential solutions.
  • Fitness Function: A method to evaluate how well a solution solves the problem.
  • Selection: The process of choosing the best solutions for reproduction.
  • Crossover: Combining two solutions to create a new solution.
  • Mutation: Randomly altering a solution to maintain genetic diversity.

Practical Tip: Familiarize yourself with biological terms as they relate to GAs, such as selection and reproduction, to better understand the algorithm's mechanics.

Step 2: Setting Up MATLAB

• Ensure you have MATLAB installed on your computer. If you are new to MATLAB, consider exploring introductory resources or playlists.
• Access the source code for Genetic Algorithms from the provided GitHub repository: Kelas Terbuka GitHub.

Common Pitfall: Make sure your MATLAB version is compatible with any functions or toolboxes you plan to use.

Step 3: Implementing a Basic Genetic Algorithm

• Start by defining the parameters for your GA:

  • Population size
  • Number of generations
  • Crossover and mutation rates

• Create a basic structure in MATLAB for your GA. Here’s a simple skeleton code to get started:

function ga_algorithm()
    population_size = 100; % Example population size
    generations = 50;      % Number of generations
    crossover_rate = 0.7;  % Crossover probability
    mutation_rate = 0.01;  % Mutation probability
    
    % Initialize population
    population = initialize_population(population_size);
    
    for gen = 1:generations
        % Evaluate fitness
        fitness = evaluate_fitness(population);
        
        % Selection
        selected = selection(population, fitness);
        
        % Crossover
        offspring = crossover(selected, crossover_rate);
        
        % Mutation
        mutated_offspring = mutation(offspring, mutation_rate);
        
        % Create new population
        population = [selected; mutated_offspring];
    end
end

Practical Tip: Test each component (initialization, selection, crossover, mutation) separately before integrating them into the main function.

Step 4: Evaluating Results

• After running your GA, analyze the output to determine the best solutions found.
• Use visualizations (such as plots) to track the progress of your fitness over generations.
• Adjust parameters based on results to improve performance.

Common Pitfall: If results are not as expected, revisit your fitness function and ensure it accurately reflects the goals of your optimization problem.

Conclusion

This tutorial introduced the basics of Genetic Algorithms and provided a foundational framework for implementation in MATLAB. As you progress, consider exploring advanced topics such as adaptive genetic algorithms or hybrid approaches. Keep practicing, and don't hesitate to engage with the Kelas Terbuka community for support and resources. Happy coding!