gan-evolution-engine

# 🧬 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 ```bash # 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 <target-skill> --generations 10 --population 20 ``` ### CLI Options | Flag | Description | Default | |------|-------------|---------| | `--skill` | Target skill to evolve | required | | `--generations` | Number of evolution cycles | 10 | | `--population` | Population size per generation | 20 | | `--elite-ratio` | Fraction of elite variants to keep | 0.2 | | `--mutation-rate` | Probability of mutation | 0.1 | | `--output` | Output directory for evolved skills | `evolved/` | | `--promote` | Auto-promote top variants to production | false | | `--publish` | Submit top capsule to EvoMap | false | --- ## 🔬 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/<skill>-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 | Risk | Mitigation | |------|------------| | **Degenerate Skills** | Validation suite runs before evaluation; invalid variants discarded | | **Infinite Loop** | Hard generation limit; timeout per variant (5min) | | **Performance Regression** | Require fitness > baseline before promotion | | **Code Injection** | Sandboxed execution; no network access for variants | | **Resource Exhaustion** | Population size capped at 100; parallel evaluations limited | --- ## 🧪 Testing Run unit tests: ```bash python3 -m pytest tests/gan_evolution/ -v ``` --- ## 📜 License MIT --- *"Evolution is not random mutation alone; it's the selective amplification of success."*