Evolution Simulation

A Python-based evolutionary simulation where autonomous agents navigate a grid-based environment, consume food sources, reproduce, and evolve over time. The project uses neural network decision making for agent behavior and tracks population statistics through real time charts.

Features

• Agent-Based Simulation: Autonomous agents with neural network brains making movement decisions
• Evolutionary Dynamics: Agents reproduce with genetic crossover and mutations, leading to different behaviors
• Dynamic Environment: Multiple food sources (grass patches, berry bushes, fruit trees, cactus pads) with regeneration
• Real Time Visualization: Pygame-based UI showing the grid state and population charts
• Statistics & Monitoring: Live charts tracking population age, energy levels, and other metrics
• Configurable Parameters: YAML-based configuration for world size, mating rules, mutation rates, and more

How It Works

1. Agents spawn in the environment and consume food to gain energy
2. Decision Making is governed by neural networks with inputs from food and agent sensing
3. Mating occurs when nearby agents of the same group have sufficient energy and age
4. Evolution happens through genetic crossover and mutations in offspring
5. Visualization displays the grid with agents and food, plus real time population statistics

Setup

# Create virtual environment (first time only)
python3 -m venv venv

# Activate virtual environment
# On Linux/Mac:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

# Install dependencies
pip3 install -r requirements.txt

# Run the simulation
python3 src/run.py

Dependencies

• pygame - Graphics and event handling
• pygame_gui - UI components
• PyYAML - Configuration file parsing

Configuration

Edit config.yaml to adjust:

• Window and panel dimensions
• Grid cell size
• Mating parameters (age threshold, energy level, mutation rates)
• Chart display settings

Project Structure

• src/agent.py - Agent class with neural network decision making and genetics
• src/environment.py - Main simulation state and update logic
• src/area.py - Grid-based spatial representation
• src/food.py - Food source types and behavior
• src/mating.py - Reproduction and genetic inheritance logic
• src/terrain.py - World generation and topology
• src/ui.py - User interface management
• src/charts.py - Statistics and visualization
• src/sense.py - Agent sensory input processing

Notes

• GitHub Actions runs pylint checks on all pull requests
• The simulation uses torus topology (wraps at edges)