https://github.com/naveen88112/genetic_algos_maze_solver

Genetic Algorithm Maze Solver This project utilizes a Genetic Algorithm (GA) to solve the optimization of a maze problem. It evolves the population of promising solutions to the best solution finding shortest path between start and end point using selection, crossover, and mutation.
https://github.com/naveen88112/genetic_algos_maze_solver

deap-library genetic-algorithms matplotlib maze-solver numpy python

Last synced: 3 months ago
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Genetic Algorithm Maze Solver This project utilizes a Genetic Algorithm (GA) to solve the optimization of a maze problem. It evolves the population of promising solutions to the best solution finding shortest path between start and end point using selection, crossover, and mutation.

• Host: GitHub
• URL: https://github.com/naveen88112/genetic_algos_maze_solver
• Owner: Naveen88112
• Created: 2025-03-11T08:09:29.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-03-11T09:23:19.000Z (3 months ago)
• Last Synced: 2025-03-11T10:33:17.376Z (3 months ago)
• Topics: deap-library, genetic-algorithms, matplotlib, maze-solver, numpy, python
• Language: Jupyter Notebook
• Homepage:
• Size: 39.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Genetic Algorithm Maze Solver

Overview

This project implements a Genetic Algorithm (GA) to solve a maze optimization problem. It evolves a population of potential solutions to find the shortest path from the start to the goal using selection, crossover, and mutation techniques.

Features

- Maze Representation: The maze is represented as a grid with walls and open paths.

- Genetic Algorithm Implementation: Uses DEAP for evolutionary computation.

- Fitness Evaluation: Solutions are evaluated based on path length and successful goal completion.

- Mutation & Crossover: Random mutations and crossovers enhance genetic diversity.

- Visualization: Matplotlib is used to display the maze and the solution path.

Technologies Used

- Python

- DEAP (Distributed Evolutionary Algorithms in Python)

- NumPy

- Matplotlib

How to Run

1. Clone the repository:

   

   "git clone https://github.com/yourusername/genetic-algo-maze-solver.git"

   

2. Install dependencies:

   

   "pip install matplotlib deap numpy"

3. Run the Jupyter Notebook or execute the script:

   

   "python maze_solver.py"

Results & Insights

- The genetic algorithm evolves solutions to navigate through the maze.

- Mutation and crossover help refine the paths for better solutions.

- Visualization provides insights into how the algorithm explores the search space.