[MATLAB] Algoritma Genetika #1 - Genetik dan Fitness Value

Introduction

This tutorial will guide you through the basics of genetic algorithms and fitness value computation using MATLAB. By the end of this tutorial, you will understand how to represent genetic information and implement a fitness function effectively, ensuring your computations converge towards an optimal solution.

Step 1: Understanding Genetic Representation

Genetic algorithms often use binary representations or other suitable encoding methods for individuals in a population. Here’s how to approach this:

• Choose a Representation Method: Common methods include:
  • Binary strings (e.g., 101010)
  • Real numbers
  • Permutations
• Define Chromosomes: Each individual in the population is represented by a chromosome. Ensure the representation aligns with the problem you are solving.

Practical Tip

Select a representation that simplifies the optimization problem. For example, if you are optimizing real-valued functions, consider using real number representations.

Step 2: Implementing the Fitness Function

The fitness function evaluates how good an individual solution is. Follow these steps to create a fitness function:

• Define the Objective: Determine what you want to optimize (e.g., minimize cost, maximize efficiency).
• Create the Function: In MATLAB, you can define your fitness function using a separate file or anonymous function. An example fitness function might look like this:

function fitness = myFitnessFunction(chromosome)
    % Example objective: maximize the sum of the chromosome values
    fitness = sum(chromosome);
end

Common Pitfalls

• Ensure your fitness function accurately reflects the objectives. A poorly defined fitness function can lead to suboptimal solutions.

Step 3: Population Initialization

Start with a diverse population to ensure broad exploration of the solution space.

• Generate Random Individuals: Use MATLAB’s random number generation functions to create initial chromosomes.
• Set Population Size: Decide the number of individuals in your population, balancing between computational efficiency and solution diversity.

Example Code

Here’s a simple way to initialize a population of binary chromosomes:

populationSize = 100; % Number of individuals
chromosomeLength = 10; % Length of each chromosome
population = randi([0, 1], populationSize, chromosomeLength);

Step 4: Selection Process

Select individuals for reproduction based on their fitness scores.

• Methods of Selection:
  • Roulette Wheel Selection: Higher fitness increases the chance of selection.
  • Tournament Selection: Randomly select a few individuals and choose the best among them.

Practical Advice

Experiment with different selection methods to find the most effective one for your specific problem.

Step 5: Crossover and Mutation

Introduce genetic diversity and facilitate exploration of the solution space through crossover and mutation.

• Crossover: Combine parts of two parent chromosomes to produce offspring.

  • Example Crossover Code:

  function offspring = crossover(parent1, parent2)
      crossoverPoint = randi(length(parent1)-1);
      offspring = [parent1(1:crossoverPoint), parent2(crossoverPoint+1:end)];
  end
  

• Mutation: Randomly alter bits in a chromosome to maintain diversity.

  • Example Mutation Code:

  function mutated = mutate(chromosome, mutationRate)
      for i = 1:length(chromosome)
          if rand < mutationRate
              chromosome(i) = ~chromosome(i); % Flip the bit
          end
      end
      mutated = chromosome;
  end
  

Conclusion

In this tutorial, you've learned the foundational steps of implementing a genetic algorithm in MATLAB, including genetic representation, fitness function design, population initialization, selection methods, and genetic operations like crossover and mutation.

To further enhance your understanding, consider exploring the provided playlists and resources from Kelas Terbuka. Keep practicing with different optimization problems to solidify your skills. Happy coding!