Box Evolution Simulation

A Pygame-based simulation where boxes evolve using a genetic algorithm to navigate obstacles and reach a goal.

Description

• Each box has a set of "genes" representing decision points (jump or no action) at specific horizontal positions.
• Boxes move automatically from left to right, jumping when instructed by their genes.
• The goal is to reach the end while avoiding obstacles.
• Fitness is calculated based on obstacles cleared, reaching the goal, and penalizing unnecessary jumps.
• A genetic algorithm evolves the population over generations, using crossover and mutation to improve performance.

Features

• Real-time simulation using Pygame.
• Genetic algorithm with:
  • Roulette wheel selection
  • One-point crossover
  • Mutation introducing jumps near parent death
• Visual representation of boxes, obstacles, and goal.
• Console output showing the best fitness per generation.

Configuration

• WIDTH, HEIGHT : Window dimensions (default 800x400)
• GROUND : Ground level
• POP_SIZE : Number of boxes per generation
• GENE_COUNT : Number of decision points per box
• FPS : Frames per second
• BONUS : Fitness bonus per cleared obstacle
• JUMP_PENALTY : Fitness penalty per jump
• MUTATION_PROB : Probability to introduce new jump near parent death
• HORIZONTAL_SPEED : Box horizontal movement speed
• GRAVITY : Vertical acceleration due to gravity
• JUMP_VELOCITY : Initial vertical velocity for jumps

Requirements

• Python 3.x
• Pygame (pip install pygame)

Running the Simulation

1. Install dependencies:

pip install pygame

2. Run the script:

python main.py

3. Watch boxes attempt to navigate obstacles in real-time. Console output shows the best fitness per generation.

Notes

• Boxes start with random gene sequences.
• Over generations, the population adapts to clear obstacles more effectively.
• Mutation occasionally introduces jumps near the position where parent boxes died, helping the evolution process.