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https://github.com/talfig/game-of-life-conway
Conway's Game Of Life simulation with genetic algorithm
https://github.com/talfig/game-of-life-conway
biological-computing game-of-life genetic-algorithm genetic-programming
Last synced: 12 days ago
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Conway's Game Of Life simulation with genetic algorithm
- Host: GitHub
- URL: https://github.com/talfig/game-of-life-conway
- Owner: talfig
- Created: 2024-12-18T20:15:36.000Z (16 days ago)
- Default Branch: main
- Last Pushed: 2024-12-18T20:33:47.000Z (16 days ago)
- Last Synced: 2024-12-18T21:23:55.966Z (16 days ago)
- Topics: biological-computing, game-of-life, genetic-algorithm, genetic-programming
- Language: Python
- Homepage:
- Size: 29.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Game of Life Simulation with Genetic Algorithm
This project simulates the "Game of Life" using a grid, and employs a genetic algorithm to evolve grid patterns. The goal is to evolve a grid pattern that can develop into a "Methuselah" (a pattern that lives for many generations before reaching a stable state) by using evolutionary techniques like selection, crossover, and mutation.
## Project Structure
The project is organized into several modules, each responsible for different parts of the simulation:
- **grid.py**: Contains the `LifeGrid` class, which models the grid and manages the evolution of cell patterns based on the Game of Life rules. It implements functions for initializing the grid, applying the rules, and running the simulation for a number of generations.
- **genetic_algorithm.py**: Implements the genetic algorithm to evolve `LifeGrid` patterns. This class contains methods for initializing the population, selecting the fittest individuals, performing crossover, and mutation to generate new grids that might evolve into a Methuselah.
- **simulation.py**: Runs simulations of `LifeGrid` evolution, tracks the maximum size of patterns, and evaluates the fitness of the grid configurations by simulating their evolution and calculating the maximum size of alive cells.
- **pattern.py**: Handles saving and loading patterns to/from TOML files. This includes the ability to serialize and deserialize patterns, making it easier to save the state of a grid and resume simulations.
- **gui_simulation.py**: Provides a GUI interface using `tkinter` to visually simulate the Game of Life. The `GuiSimulation` class creates a grid of cells, where each cell is either alive (black) or dead (white). The simulation can be started and stopped with buttons, and the current generation and number of alive cells are displayed in real time.
Each module encapsulates specific functionality like grid management, genetic algorithm operations, pattern handling, and visualization through a graphical interface.
## How It Works
### 1. Genetic Algorithm
The genetic algorithm works by initializing a population of random `LifeGrid` configurations. Each individual grid's fitness is calculated by running a simulation and measuring the maximum size the pattern reaches. The algorithm selects the fittest individuals for reproduction using crossover and mutation to generate the next generation. The goal is to evolve a grid pattern that exceeds a certain fitness threshold (Methuselah).
### 2. Simulation
The `LifeGrid` class is the core of the simulation. It applies the Game of Life rules to evolve a pattern. The grid evolves by checking the number of neighbors for each cell and deciding whether the cell survives or dies based on the classic Game of Life rules.
### 3. Visualization
A simple `tkinter` GUI displays the simulation in real time. The `GuiSimulation` class creates a grid of cells, where each cell is either alive (black) or dead (white). The simulation can be started and stopped with buttons, and the current generation and number of alive cells are displayed.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/game-of-life-conway.git
```
2. Install dependencies:
```bash
pip install toml
```
## Usage
To run the genetic algorithm and find a Methuselah pattern, you can execute the `genetic_algorithm.py` script:
```bash
python rplife/genetic_algorithm.py
```
To run the demo:
```bash
python demo.py
```
## License
This project is licensed under the MIT License - see the [LICENSE](https://github.com/talfig/Game-of-Life-Conway/blob/main/LICENSE) file for details.