Genetic Algorithm for the Knapsack Problem

This project implements a genetic algorithm to solve the Knapsack Problem, a classic problem in combinatorial optimization. The goal is to determine the most valuable combination of items to include in a knapsack without exceeding its weight capacity.

Features

• Modular Code Structure: Organized into separate modules for different aspects of the genetic algorithm, such as selection, crossover, mutation, and fitness calculation.
• Customizable Parameters: Easy adjustment of algorithm parameters like population size, mutation rate, and number of generations.
• Knapsack Problem Implementation: Demonstrates the application of genetic algorithms to a well-known optimization problem.

Project Structure

• src/main.rs: The main entry point of the program.
• src/item.rs: Defines the Item struct.
• src/ga/: Contains modules related to the genetic algorithm.
  • chromosome.rs: Represents a solution.
  • fitness.rs: Fitness function for the algorithm.
  • selection.rs: Selection process for breeding.
  • crossover.rs: Crossover function for generating new offspring.
  • mutation.rs: Mutation function to introduce variability.
• src/constraints.rs: Defines the constraints for the Knapsack Problem.
• src/variables.rs: Contains the definition and initialization of items.

Usage

1. Setup: Clone the repository and navigate to the project directory.
2. Build: Run cargo build to compile the project.
3. Run: Execute the program with cargo run.

Customization

• Items: Modify the items in src/variables.rs to experiment with different sets of items for the knapsack.
• Constraints: Adjust the genetic algorithm parameters in src/constraints.rs like max_weight, population_size, generations, and mutation_rate.

Dependencies

• rand: Used for generating random numbers in various parts of the algorithm.

License

This project is licensed under the MIT License.

Contributing

Contributions are welcome! Please feel free to submit a pull request or create an issue for any improvements or bug fixes.