This project is part of the Artificial Intelligence course for the academic year 2022/2023. It explores various methods to solve the N-Queens problem, including Random Search, Backtracking, Hill-Climbing, Simulated Annealing, and Genetic Algorithms. Each method is evaluated for its efficiency and effectiveness in finding solutions.

The N-Queens problem is a classic puzzle in which the challenge is to place N queens on an N x N chessboard in such a way that no two queens threaten each other. This means that no two queens can be placed in the same row, column, or diagonal. The significance of this problem in Artificial Intelligence (AI) lies in its use as a benchmark for evaluating algorithms, particularly those involving backtracking and search strategies.

The N-Queens problem was approached using various methods, each with different levels of success and efficiency:

• Random Search: Struggles with even small search spaces (N = 8) with a 40% success rate.
• Backtracking: Performs well in smaller spaces (N = 24) with a 100% success rate, but fails in larger spaces (N = 32).
• Standard Hill Climbing: Fails to solve even N = 8 due to local optima.
• Hill Climbing with Sideways Moves: Can solve larger problems (N = 60) but takes longer (55 seconds).
• Stochastic Hill Climbing with Sideways Moves: Improves results up to N = 60.
• Simulated Annealing: Most efficient, solving N = 60 in 10 seconds and N = 100 in 50 seconds, demonstrating scalability.
• Genetic Algorithms: Least efficient, struggling with N = 8 even with hyperparameter tuning.

These results highlight the efficiency and scalability of Simulated Annealing for larger instances of the N-Queens problem.

• Problem Description
• Board
  • Board Generation
    • One per Column
    • One per Row
  • Board Visualization
• Count the Number of Conflicts
  • $O(n^2)$ Solution
  • $O(n)$ Solution
  • Speed Comparison
• Methods
  • Random Search
  • Backtracking
  • Hill-Climbing
    • Hill-Climbing without Sideways Moves
    • Stochastic Hill-Climbing with Sideways Moves
    • Random-Restart Hill-Climbing
  • Simulated Annealing
  • Genetic Algorithms
    • Hyperparameter Tuning
• Conclusion

For detailed information, please refer to the N-Queens.ipynb file.