This report documents the performance and evolution of the "Self-cloning AI agents" system.
- Population: 50 Agents (Start with 5)
- Generations: 10
- Trials: 5 independent runs
- Selection: Top 50% survive
- Reproduction: Top agents clone themselves with mutation.
- Population Growth: The system successfully scales from 5 initial agents to the maximum capacity of 50 within 1 generation.
- Score Improvement:
- Initial generations often start with scores around 120-130.
- By generation 10, best scores consistently reach 150-160.
- This indicates that the "Self-cloning" and "Competition" mechanism effectively evolves better strategies.
- Strategy Convergence:
- Agents tend to converge towards higher
focus(~0.4-0.5) and higheriteration(10-12) values, which yields higher mock scores.
- Agents tend to converge towards higher
The "One becomes many" architecture is functional. Agents demonstrate autonomous evolution towards optimal solutions through self-cloning and competition.
- Integrate real LLM for complex problem solving.
- Implement distributed processing for massive agent populations.