Genetic Algorithm Class

This repository contains a Python implementation of a Genetic Algorithm (GA) class and an example application solving the Traveling Salesman Problem (TSP).

Contents

1. Class_GA.py: Contains the GeneticAlgorithm class, a flexible implementation of a genetic algorithm.
2. TS_example.py: Demonstrates how to use the GeneticAlgorithm class to solve a Traveling Salesman Problem.

How to Use

Requirements

• Python 3.6 or higher
• No additional libraries required

Running the TSP Example

1. Clone this repository:

   git clone https://github.com/yourusername/ga-tsp-solver.git
   cd ga-tsp-solver
   

2. Run the TSP example:

   python TS_example.py
   

This will execute the genetic algorithm to find an optimal solution for the TSP defined in the script.

Using the GeneticAlgorithm Class

To use the GeneticAlgorithm class for your own problems:

1. Import the class:

   from Class_GA import GeneticAlgorithm

2. Define your fitness function. It should take a chromosome (list of integers) as input and return a fitness score (float).

3. Initialize the GA:

   ga = GeneticAlgorithm(
       population_size=100,
       chromosome_length=your_chromosome_length,
       fitness_func=your_fitness_function,
       mutation_rate=0.02,
       crossover_rate=0.8,
       elitism=0.1
   )

4. Evolve the population:

   best_solution = ga.evolve(generations=500)

5. Interpret the result based on your problem domain.

Customization

• Modify the mutation_rate, crossover_rate, and elitism parameters to fine-tune the algorithm's performance.
• Adjust the population_size and number of generations based on your problem's complexity and computational resources.
• For problems other than permutation-based ones like TSP, you may need to modify the generate_chromosome, crossover, and mutate methods in the GeneticAlgorithm class.

Contributing

Contributions to improve the algorithm or add new features are welcome. Please feel free to submit pull requests or open issues for any bugs or enhancements.

License

This project is open-source and available under the MIT License.