A Python project for optimizing the 8 Queens Puzzle using the Genetic Algorithm implemented in PyGAD.

Overview

8QueensGenetic

A Python project for optimizing the 8 Queens Puzzle using the Genetic Algorithm implemented in PyGAD.

The project uses the Kivy cross-platform Python framework for building the GUI of the 8 queens puzzle. The GUI helps to visualize the solutions reached while the genetic algorithm (GA) is optimizing the problem to find the best solution.

For implementing the genetic algorithm, the PyGAD library is used. Check its documentation here: https://pygad.readthedocs.io

IMPORTANT If you are coming for the code of the tutorial 8 Queen Puzzle Optimization Using a Genetic Algorithm in Python, then it has been moved to the TutorialProject directory on 17 June 2020.

PyGAD Installation

To install PyGAD, simply use pip to download and install the library from PyPI (Python Package Index). The library lives a PyPI at this page https://pypi.org/project/pygad.

For Windows, issue the following command:

pip install pygad

For Linux and Mac, replace pip by use pip3 because the library only supports Python 3.

pip3 install pygad

PyGAD is developed in Python 3.7.3 and depends on NumPy for creating and manipulating arrays and Matplotlib for creating figures. The exact NumPy version used in developing PyGAD is 1.16.4. For Matplotlib, the version is 3.1.0.

Project GUI

The project comes with a GUI built in Kivy, a cross-platform Python framework for building natural user interfaces. Before using the project, install Kivy:

pip install kivy

Because the project is built using Python 3, use pip3 instead of pip for Mac/Linux:

pip3 install kivy

Check this Stackoverflow answer to install other libraries that are essential to run Kivy: https://stackoverflow.com/a/44220712

The main file for this project is called main.py which holds the code for building the GUI and instantiating PyGAD for running the genetic algorithm.

After running the main.py file successfully, the window will appear as given in the figure below. The GUI uses a GridLayout for creating an 8x8 grid. This grid represents the board of the 8 queen puzzle.

main

The objective of the GA is to find the best locations for the 8 queens so that no queen is attacking another horizontally, vertically, or diagonally. This project assumes that no 2 queens are in the same row. As a result, we are sure that no 2 queens will attack each other horizontally. This leaves us to the 2 other types of attacks (vertically and diagonally).

The bottom part of the window has 3 Button widgets and 1 Label widget. From left to right, the description of the 3 Button widgets is as follows:

  • The Initial Population button creates the initial population of the GA.
  • The Show Best Solution button shows the best solution in the last generation the GA stopped at.
  • The Start GA button starts the GA iterations/generations.

The Label widget just prints some informational messages to the user. For example, it prints the fitness value of the best solution when the user presses the Show Best Solution button.

Steps to Use the Project

Follow these steps to use the project:

  1. Run the main.py file.
  2. Press the Initial Population Button.
  3. Press the Start GA Button.

After pressing the Start GA button, the GA uses the initial population and evolves its solutions until reaching the best possible solution.

Behind the scenes, some important stuff was built that includes building the Kivy GUI, instantiating PyGAD, preparing the the fitness function, preparing the callback function, and more. For more information, please check the tutorial titled 8 Queen Puzzle Optimization Using a Genetic Algorithm in Python.

6 Attacks

After running the main.py file and pressing the Initial Population button, the next figure shows one possible initial population in which 6 out of 8 queens are attacking each other.

1  6 attacks

In the Label, the fitness value is calculated as 1.0/number of attacks. In this case, the fitness value is equal to 1.0/6.0 which is 0.1667.

The next figures shows how the GA evolves the solutions until reaching the best solution in which 0 attacks exists.

5 Attacks

2  5 attacks

4 Attacks

3  4 attacks

3 Attacks

4  3 attacks

2 Attacks

5  2 attacks

1 Attack

6  1 attack

0 Attacks (Optimal Solution)

7  0 attack

IMPORTANT

It is very important to note that the GA does not guarantee reaching the optimal solution each time it works. You can make changes in the number of solutions per population, the number of generations, or the number of mutations. Other than doing that, the initial population might also be another factor for not reaching the optimal solution for a given trial.

For More Information

There are different resources that can be used to get started with the building CNN and its Python implementation.

Tutorial: 8 Queen Puzzle Optimization Using a Genetic Algorithm in Python

In 1 May 2019, I wrote a tutorial discussing this project. The tutorial is titled 8 Queen Puzzle Optimization Using a Genetic Algorithm in Python which is published at Heartbeat. Check it at these links:

Tutorial Cover Image

Book: Practical Computer Vision Applications Using Deep Learning with CNNs

You can also check my book cited as Ahmed Fawzy Gad 'Practical Computer Vision Applications Using Deep Learning with CNNs'. Dec. 2018, Apress, 978-1-4842-4167-7 which discusses neural networks, convolutional neural networks, deep learning, genetic algorithm, and more.

Find the book at these links:

Fig04

Citing PyGAD - Bibtex Formatted Citation

If you used PyGAD, please consider adding a citation to the following paper about PyGAD:

@misc{gad2021pygad,
      title={PyGAD: An Intuitive Genetic Algorithm Python Library}, 
      author={Ahmed Fawzy Gad},
      year={2021},
      eprint={2106.06158},
      archivePrefix={arXiv},
      primaryClass={cs.NE}
}

Contact Us

You might also like...
8 Puzzle with A* , Greedy & BFS Search in Python

8_Puzzle 8 Puzzle with A* , Greedy & BFS Search in Python Python Install Python from here. Pip Install pip from here. How to run? 🚀 Install 8_Puzzle

A tictactoe where you never win, implemented using minimax algorithm
A tictactoe where you never win, implemented using minimax algorithm

Unbeatable_TicTacToe A tictactoe where you never win, implemented using minimax algorithm Requirements Make sure you have the pygame module along with

CLI Eight Puzzle mini-game featuring BFS, DFS, Greedy and A* searches as solver algorithms.
CLI Eight Puzzle mini-game featuring BFS, DFS, Greedy and A* searches as solver algorithms.

🕹 Eight Puzzle CLI Jogo do quebra-cabeças de 8 peças em linha de comando desenvolvido para a disciplina de Inteligência Artificial. Escrito em python

Genetic algorithms are heuristic search algorithms inspired by the process that supports the evolution of life.

Genetic algorithms are heuristic search algorithms inspired by the process that supports the evolution of life. The algorithm is designed to replicate the natural selection process to carry generation, i.e. survival of the fittest of beings.

Using A * search algorithm and GBFS search algorithm to solve the Romanian problem
Using A * search algorithm and GBFS search algorithm to solve the Romanian problem

Romanian-problem-using-Astar-and-GBFS Using A * search algorithm and GBFS search algorithm to solve the Romanian problem Romanian problem: The agent i

An implementation of ordered dithering algorithm in python as multimedia course project
An implementation of ordered dithering algorithm in python as multimedia course project

One way of minimizing the size of an image is to simply reduce the number of bits you use to represent each pixel.

All Algorithms implemented in Python

The Algorithms - Python All algorithms implemented in Python (for education) These implementations are for learning purposes only. Therefore they may

Algorithms implemented in Python

Python Algorithms Library Laurent Luce Description The purpose of this library is to help you with common algorithms like: A* path finding. String Mat

All algorithms implemented in Python for education

The Algorithms - Python All algorithms implemented in Python - for education Implementations are for learning purposes only. As they may be less effic

Owner
Ahmed Gad
Ph.D. Student at uOttawa // Machine Learning Researcher & Technical Author https://amazon.com/author/ahmedgad
Ahmed Gad
This is an Airport Scheduling Time table implemented using Genetic Algorithm

This is an Airport Scheduling Time table implemented using Genetic Algorithm In this The scheduling is performed on the basisi of that no two Air planes are arriving or departing at the same runway at the same time and day there are total of 4 Airplanes 3 and 3 Runways.

null 1 Jan 6, 2022
A simple python implementation of A* and bfs algorithm solving Eight-Puzzle

A simple python implementation of A* and bfs algorithm solving Eight-Puzzle

null 2 May 22, 2022
8-puzzle-solver with UCS, ILS, IDA* algorithm

Eight Puzzle 8-puzzle-solver with UCS, ILS, IDA* algorithm pre-usage requirements python3 python3-pip virtualenv prepare enviroment virtualenv -p pyth

Mohsen Arzani 4 Sep 22, 2021
🧬 Training the car to do self-parking using a genetic algorithm

?? Training the car to do self-parking using a genetic algorithm

Oleksii Trekhleb 652 Jan 3, 2023
N Queen Problem using Genetic Algorithm

The N Queen is the problem of placing N chess queens on an N×N chessboard so that no two queens attack each other.

Mahdi Hassanzadeh 2 Nov 11, 2022
A genetic algorithm written in Python for educational purposes.

Genea: A Genetic Algorithm in Python Genea is a Genetic Algorithm written in Python, for educational purposes. I started writing it for fun, while lea

Dom De Felice 20 Jul 6, 2022
Genetic Algorithm for Robby Robot based on Complexity a Guided Tour by Melanie Mitchell

Robby Robot Genetic Algorithm A Genetic Algorithm based Robby the Robot in Chapter 9 of Melanie Mitchell's book Complexity: A Guided Tour Description

Matthew 2 Dec 1, 2022
Genetic algorithm which evolves aoe2 DE ai scripts

AlphaScripter Use the power of genetic algorithms to evolve AI scripts for Age of Empires II : Definitive Edition. For now this package runs in AOC Us

null 6 Nov 4, 2022
An NUS timetable generator which uses a genetic algorithm to optimise timetables to suit the needs of NUS students.

A timetable optimiser for NUS which uses an evolutionary algorithm to "breed" a timetable suited to your needs.

Nicholas Lee 3 Jan 9, 2022
Genius Square puzzle solver in Python

Genius Square puzzle solver in Python

James 3 Dec 15, 2022