RoboPhD: Evolving Diverse Complex Agents Under Tight Evaluation Budgets

1. 1Identify the optimization algorithm to use for evolving agents
2. 2Determine the evaluation budget and its constraints
3. 3Apply LLM-guided evolution to iteratively improve agent architectures and prompts
4. 4Compare the performance of different optimization algorithms under the same evaluation budget

ML researchers and AI engineers benefit from this research as it provides insights into optimizing agent evolution under limited evaluation budgets, which can be applied to various domains

💡 LLM-guided evolution can be used to optimize agent evolution under tight evaluation budgets

🤖 Evolve complex agents with limited evaluations! 📊