Artificial Intelligence Neuroevolution (NEAT) Validated

Neuroevolution (NEAT)

Neural networks that learn through natural selection

Fitness History

Neural Network

Statistics
Generation
0
Best Score
0
Alive
0
Settings
Batch Training

Neuroevolution

This demo uses neuroevolution: neural networks trained by genetic algorithms, without backpropagation.

Neural Network

Each bird has a small neural network (brain) that decides when to jump:

  1. Inputs (4 neurons): bird position, velocity, pipe distance, gap position
  2. Hidden layer(s): configurable neurons with activation function
  3. Output (1 neuron): jump if value > 0.5

Genetic Algorithm

Instead of gradient descent, evolution optimizes the weights:

  1. Evaluation: Each bird plays until it dies. Score = frames survived.
  2. Selection: Best birds (highest scores) are selected as parents.
  3. Crossover: Two parents combine their weights to create offspring.
  4. Mutation: Random small changes to weights (exploration).
  5. Repeat: New generation plays, best survive, evolution continues.

Key Parameters

  • Population: More = better exploration, slower
  • Mutation rate: High = exploration, Low = exploitation
  • Elite count: Best N birds copied unchanged

Why It Works

  • No labels needed: The score (survival time) is the only feedback
  • Parallel search: Many solutions tested simultaneously
  • Emergent behavior: Complex strategies from simple rules
© 2013 - 2026 Cylian Size:  T S M L W Theme:  Dark Light [Detected] [Forced] 0.62.0 (f7ef9462) 🤖 Claude
About

Generation Prompt

Page: Neuroevolution - Flappy Bird AI
Slogan: "Neural networks that learn through natural selection"

Structure:
- Widget before:title → h1 + p.slogan centered
- Widget left:history → Fitness history graph (canvas)
- Widget left:network → Neural network visualization (canvas)
- Widget right:stats → Statistics (generation, best score, alive)
- Widget right:controls → Settings + Play/Reset + Batch training
- Main → section.neuro-container with canvas 600×400
- Widget after:algorithm → Algorithm explanation
- Widget modal:claude → Documentation

Controls:
- Population: 10-200 (default 50)
- Mutation: 0-100% slider (default 20%)
- Network: dropdown (6/8/8→4/4→8/6→6/8→6→4)
- Activation: dropdown (ReLU/Tanh/Sigmoid)
- Speed: dropdown (0.5x/1x/2x/4x)
- Show Best: checkbox
- Play/Pause + Reset buttons
- Batch training: count input + Train button

Keyboard:
- Space: toggle play/pause
- Escape: stop batch training

Game loop:
1. Birds decide to jump based on neural network output
2. Physics: gravity, velocity, collision detection
3. Dead birds removed, score = frames survived
4. All dead → next generation (selection, crossover, mutation)
5. Max score (65535) → auto-win, end generation