🧬 GAN Evolution Engine

"进化即对抗：生成变异，判别优劣，迭代超越"

The GAN Evolution Engine implements a generative adversarial approach to skill evolution. Instead of random mutations, it uses a learned Generator to propose skill modifications and a Discriminator to evaluate their fitness based on runtime metrics and user feedback.

✨ Features

• 🎲 Generator Network: LLM-powered generation of skill variants (code, prompts, logic)
• ⚖️ Discriminator: Performance-based fitness evaluation (accuracy, speed, user satisfaction)
• 🔁 Adversarial Loop: Generator vs Discriminator co-evolution drives rapid improvement
• 📈 Population Management: Maintains diverse pool of skill variants
• 🚀 Elite Selection: Top-performing variants become candidates for promotion
• 📊 Integration: Seamless integration with evomap-publish for capsule submission

🏗️ Architecture

┌─────────────────┐     ┌──────────────────┐
│   Generator     │     │  Discriminator   │
│  (LLM Agent)    │────▶│  (Perf Analyzer) │
└─────────────────┘     └──────────────────┘
         │                        │
         ▼                        ▼
   Skill Variants          Fitness Scores
         │                        │
         └────────┬───────────────┘
                  ▼
         Selection & Crossover
                  │
                  ▼
           Next Generation

📦 Usage

Quick Start

# 1. Ensure evomap-publish is configured
mkdir -p ~/.evomap
echo "node_db2f95ffdba95eb6" > ~/.evomap/node_id
echo "d846e0f269030e8b3eb3ed60472b164b448f8360e578a6392ccc4740d096ba14" > ~/.evomap/node_secret

# 2. Run GAN evolution cycle
python3 scripts/gan_evolution.py --skill \x3Ctarget-skill> --generations 10 --population 20

CLI Options

🔬 How It Works

1. Population Bootstrap

• Clone target skill as initial population (population=N)
• Apply random mutations to diversify initial pool

2. Generator Phase

For each generation:

• Prompt LLM with:
  • Parent skill code
  • Performance metrics (from Discriminator)
  • Mutation strategy (crossover, parameter tuning, prompt refinement)
• Generate population variant candidates

3. Evaluation Phase (Discriminator)

For each variant:

• Deploy in sandbox environment
• Run benchmark suite (accuracy, latency, resource usage)
• Collect user feedback if available
• Compute fitness score = weighted sum:

  fitness = 0.4 * accuracy + 0.3 * speed + 0.3 * feedback
  

4. Selection & Crossover

• Select top elite_ratio * population variants
• Perform crossover: combine code fragments from 2 parents
• Apply mutations to non-elite variants
• Form next generation population

5. Termination

After generations cycles:

• Select best variant (highest fitness)
• Optionally: promote to production (--promote)
• Optionally: create capsule and publish to EvoMap (--publish)

📊 Example Output

Generation 1/10
  Population: 20 variants
  Best fitness: 0.72 (variant-07)
  Avg fitness: 0.45
  Generator time: 2m 13s
  Discriminator time: 1m 42s

...

Evolution Complete! 🏆

🏆 Champion: variant-43 (fitness=0.89)
📈 Improvement: +22% over baseline
🚀 Promoted: skills/evolved/\x3Cskill>-v2/
📤 Capsule ID: sha256:abc123... (published)

⚙️ Implementation Details

Files

gan-evolution-engine/
├── SKILL.md                 # This file
├── scripts/
│   ├── gan_evolution.py    # Main orchestrator
│   ├── generator.py        # LLM-based variant generation
│   ├── discriminator.py    # Performance evaluation
│   ├── population.py       # Population management
│   └── fitness.py          # Fitness computation
└── references/
    └── prompts/            # Generator prompt templates

Key Functions

• GANEvolutionEngine.__init__(skill_path, population, generations)
• Engine.bootstrap_population(): Clone + mutate initial pool
• Engine.run_generation(): One full cycle
• Generator.generate_variant(parent, strategy): Create new variant
• Discriminator.evaluate(variant): Return fitness score 0-1
• Population.select_elites(): Top K variants
• Population.crossover(parent1, parent2): Create child

🛡️ Safety & Risk

🧪 Testing

Run unit tests:

python3 -m pytest tests/gan_evolution/ -v

📜 License

MIT

"Evolution is not random mutation alone; it's the selective amplification of success."