🧬 Evolution Simulation — Greedy vs Generous

A small, visual Python sandbox that pits two simple strategies against each other as they compete for limited food in a 2D world. The simulation demonstrates emergent population dynamics between Greedy (Red) and Generous (Blue) agents.

What it models

• Two populations start with 10 agents each: Red (Greedy) and Blue (Generous).
• Each day the world spawns a fixed number of food items (FOOD_AMOUNT). Agents move to nearest available food and occupy a left or right seat.
• Interaction rules at a food item:
  • Single occupant: eats the food (food_eaten = 1).
  • Two Blues: split food (each gets 0.5).
  • Two Reds: one randomly gets 0.5, the other gets 0.
  • Red + Blue: Red wins (Red gets 1, Blue gets 0).
• Reproduction rules each night:
  • food_eaten == 1 → produces 2 offspring.
  • food_eaten == 0.5 → produces 1 offspring.
  • food_eaten == 0 → no offspring.

These simple rules produce population shifts visible in the runtime and in the plotted graph at exit.

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Files

• main.py — Entry point and daily loop (spawning, movement, interactions, reproduction, rendering).
• blob.py — Blob class: movement, targeting, seating and drawing logic.
• food.py — Food class: seat management and drawing.
• settings.py — Constants: WIDTH, HEIGHT, colors, FOOD_AMOUNT, BLOB_SIZE, BLOB_SPEED, FOOD_SIZE, FPS.
• stats.py — Plots population over days using matplotlib.
• requirement.txt — Python packages used for plotting and graphics (pygame, matplotlib, numpy, etc.).

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Requirements

• Python 3.8+
• Packages listed in requirement.txt (pygame, matplotlib, numpy, ...)

Create a virtual environment and install dependencies:

python -m venv .venv
source .venv/bin/activate
pip install -r requirement.txt

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Run

Start the simulation with:

python main.py

• Close the Pygame window to generate and display a population graph (via matplotlib).
• The console prints day numbers and population counts during the run.

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Quick Configuration

• Edit settings.py to tune world size, FOOD_AMOUNT, BLOB_SIZE, and BLOB_SPEED.
• To change starting populations, edit the initialization loops in main.py (currently 10 red and 10 blue).

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Suggested Experiments

1. Decrease FOOD_AMOUNT to create scarcity and observe which strategy dominates.
2. Increase BLOB_SPEED for one group to test mobility advantages.
3. Modify interaction rules in main.py to explore different social dynamics.