🧩 Solving the N-Queens Problem with Exhaustive Search and Genetic Algorithms (got 40/40 points)

This repository presents a comparative study of four algorithms used to solve the N-Queens problem:

• Depth-First Search (DFS)
• Hill Climbing (HC)
• Simulated Annealing (SA)
• Genetic Algorithm (GA)

Each algorithm is evaluated in terms of solution success rate, runtime, and memory usage for various board sizes (N = 10, 30, 50, 100, 200).

🧠 Project Motivation

The N-Queens problem is a classic example of a constraint satisfaction and combinatorial optimization problem. This project explores different strategies to solve it using both exhaustive and heuristic methods.

🛠️ Running the Project

Clone the repository:

git clone https://github.com/ilnazasaifutdinova/Solving-the-N-Queens-Problem-with-Exhaustive-Search-and-Genetic-Algorithms.git

Install requirements:

pip install -r requirements.txt

Run an algorithm (example for DFS):

python src/dfs.py --n 8

📌 Key Findings

DFS is reliable for small N but quickly becomes infeasible as N grows.

Hill Climbing is fast but often gets stuck in local minima.

Simulated Annealing performs better due to probabilistic jumps.

Genetic Algorithm provides the best trade-off between speed and success rate for large N.