{"id":26201694,"url":"https://github.com/naveen88112/genetic_algos_maze_solver","last_synced_at":"2026-05-06T13:14:23.647Z","repository":{"id":281818815,"uuid":"946487631","full_name":"Naveen88112/Genetic_Algos_Maze_Solver","owner":"Naveen88112","description":"Genetic Algorithm Maze Solver This project utilizes a Genetic Algorithm (GA) to solve the optimization of a maze problem. 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It evolves a population of potential solutions to find the shortest path from the start to the goal using selection, crossover, and mutation techniques.\n\nFeatures\n- Maze Representation: The maze is represented as a grid with walls and open paths.\n- Genetic Algorithm Implementation: Uses DEAP for evolutionary computation.\n- Fitness Evaluation: Solutions are evaluated based on path length and successful goal completion.\n- Mutation \u0026 Crossover: Random mutations and crossovers enhance genetic diversity.\n- Visualization: Matplotlib is used to display the maze and the solution path.\n\nTechnologies Used\n- Python\n- DEAP (Distributed Evolutionary Algorithms in Python)\n- NumPy\n- Matplotlib\n\nHow to Run\n1. Clone the repository:\n   \n   \"git clone https://github.com/yourusername/genetic-algo-maze-solver.git\"\n   \n2. Install dependencies:\n   \n   \"pip install matplotlib deap numpy\"\n\n3. Run the Jupyter Notebook or execute the script:\n   \n   \"python maze_solver.py\"\n\nResults \u0026 Insights\n- The genetic algorithm evolves solutions to navigate through the maze.\n- Mutation and crossover help refine the paths for better solutions.\n- Visualization provides insights into how the algorithm explores the search space.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnaveen88112%2Fgenetic_algos_maze_solver","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnaveen88112%2Fgenetic_algos_maze_solver","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnaveen88112%2Fgenetic_algos_maze_solver/lists"}