The Concept
I created an interactive implementation of Conway’s Game of Life with customizable rules and real-time editing capabilities. What makes this version special is its focus on experimentation and accessibility – users can modify the simulation rules while it’s running or pause to edit patterns directly. The interface is designed to be intuitive while offering deep customization options.
Code Highlight
I’m particularly proud of the updateGrid() function, which handles the core cellular automaton logic:
function updateGrid() {
for (let i = 0; i < cols; i++) {
for (let j = 0; j < rows; j++) {
let neighbors = countNeighbors(grid, i, j);
let state = grid[i][j];if (state === 0 && neighbors === rules.birthRule) {
nextGrid[i][j] = 1;
} else if (state === 1 && (neighbors >= rules.survivalMin && neighbors <= rules.survivalMax)) {
nextGrid[i][j] = 1;
} else {
nextGrid[i][j] = 0;
}
}
}
}
This code is elegant in its simplicity while being highly flexible. Instead of hardcoding Conway’s traditional rules (birth on 3 neighbors, survival on 2 or 3), it uses variables for birth and survival conditions. This allows users to experiment with different rule sets and discover new patterns. The clean separation between rule checking and grid updating also makes it easy to modify or extend the behavior.
The Sketch
The sketch provides a full-featured cellular automata playground with:
- Real-time rule modification
- Custom color selection
- Variable brush sizes for drawing
- Grid visualization when paused
- Clear and random pattern generation
- A reset button to restore default settings
- Numerical feedback for all parameters
Reflections and Future Improvements
Working on this project revealed several interesting possibilities for future enhancements:
- Pattern Library
- Add ability to save and load interesting patterns
- Include a collection of classic Game of Life patterns (gliders, spaceships, etc.)
- Allow users to share patterns with others
- Enhanced Visualization
- Add cell age visualization (cells could change color based on how long they’ve been alive)
- Include heat maps showing areas of high activity
- Add trail effects to show pattern movement
- Extended Controls
- Add speed control for the simulation
- Implement step-by-step mode for analyzing pattern evolution
- Add undo/redo functionality for drawing
- Allow custom grid sizes
- Analysis Features
- Add population graphs showing cell count over time
- Include pattern detection to identify common structures
- Add statistics about pattern stability and periodicity
The current implementation provides a solid foundation for exploring cellular automata, but there’s still much room for expansion. The modular design makes it easy to add these features incrementally while maintaining the core functionality.
The most interesting potential improvement would be adding pattern analysis tools. Being able to track population changes and identify stable structures could help users understand how different rules affect pattern evolution. This could make the sketch not just a creative tool, but also a valuable educational resource for studying emergent behavior in cellular automata.