BayesiaNetwork

Description

This project contains a collection of Jupyter Notebook files related to optimization algorithms including Hill Climbing, Genetic Algorithm, Iterated Local Search, Simulated Annealing, and Tabu Search. Each notebook explores the implementation and application of these algorithms in solving optimization problems.

Table of Contents

• File Structure
• Installation
• Usage
• Contributing
• License

File Structure

The project directory has the following structure:

- parent directory
- ..
- HillclimbingandGeneticAlgorithm.ipynb
- Iterated Local Search and GA.ipynb
- SimulatedAnnealingandGeneticAlgorithm.ipynb
- TabusearchAndGA.ipynb

Installation

To use the Jupyter Notebook files in this project, follow these steps:

1. Clone the repository to your local machine.
2. Ensure you have Jupyter Notebook installed.
3. Open Jupyter Notebook and navigate to the cloned repository folder.
4. Open the desired notebook file (.ipynb) to explore the optimization algorithm implementation and examples.

Usage

These Jupyter Notebook files provide examples and explanations of different optimization algorithms. Each notebook focuses on a specific algorithm and provides code snippets, visualizations, and explanations to help understand and apply these algorithms in various optimization problems.

To use a specific notebook:

1. Open the desired notebook file (.ipynb) using Jupyter Notebook.
2. Read the provided explanations and code snippets.
3. Execute the code cells to see the algorithm in action or modify the code to suit your specific needs.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvement, please feel free to create a pull request or submit an issue in the repository.

License

This project is licensed under the MIT License. Feel free to use and modify the code according to the terms of this license.