One-Cell Organism Simulation

A progressive simulation of cellular organisms that evolve from basic existence to autonomous intelligence.

Development Levels

Level 1: Basic Existence

• Behavior: Static cells occupy grid positions and can only detect neighboring cells
• Abilities: Sense immediate surroundings (neighbors or obstacles)
• Algorithm Focus: Basic rendering, neighbor detection, and simple visualization

Level 2: Movement and Exploration

• Behavior: Random movement within the grid
• Abilities: Wander around the environment to explore new spaces
• Algorithm Focus: Randomized movement patterns with boundary checks

Level 3: Resource Collection

• Behavior: Detect and move toward resources
• Abilities: Consume energy from resources for movement and growth
• Algorithm Focus: Pathfinding towards nearby resources, simple prioritization

Level 4: Energy Management

• Behavior: Track energy levels for decision-making
• Abilities: Rest when energy is low, move when energy is abundant
• Algorithm Focus: Energy conservation strategies and state-based behavior

Level 5: Memory and Learning

• Behavior: Memorize explored areas, prioritize unexplored regions
• Abilities: Avoid redundant exploration, predict resource locations
• Algorithm Focus: Memory arrays/maps and basic learning heuristics

Level 6: Communication and Cooperation

• Behavior: Signal other cells about resources or dangers
• Abilities: Share information for coordinated behaviors
• Algorithm Focus: Message passing and collaborative decision-making

Level 7: Adaptation to Dynamic Changes

• Behavior: React to environmental changes
• Abilities: Adjust priorities and paths dynamically
• Algorithm Focus: Real-time recalculations and event-driven behavior

Level 8: Genetic Evolution

• Behavior: Implement mutation and reproduction mechanisms
• Abilities: Evolve efficient strategies through selection pressure
• Algorithm Focus: Genetic algorithms and fitness evaluation

Level 9: Predictive Modeling and Strategy

• Behavior: Use probabilistic models for predictions
• Abilities: Forecast changes and optimize positioning
• Algorithm Focus: Bayesian networks, Markov chains, reinforcement learning

Level 10: Autonomous Intelligence

• Behavior: Long-term planning and strategy adaptation
• Abilities: Form alliances, handle complex goals, modify algorithms
• Algorithm Focus: Neural networks and deep reinforcement learning

Energy Efficiency Considerations

Optimization Strategies

1. Minimize redundant calculations
2. Implement distributed processing
3. Use caching and memoization
4. Balance computation with asynchronous updates

Setup and Installation

1. Clone the repository
2. Open index.html in a modern web browser
3. Ensure all script dependencies are properly loaded:
   • Lodash (via CDN)
   • Local JavaScript modules

Project Structure