Melissa Pan

Recent work such as AlphaEvolve has shown that combining LLM-driven optimization with evolutionary search can effectively improve programs, prompts, and algorithms across domains. In this paradigm, previously evaluated solutions are reused to guide the model toward new candidate solutions. Crucially, the effectiveness of this evolution process depends on the search strategy: how prior solutions are selected and varied to generate new candidates. However, most existing methods rely on fixed search strategies with predefined knobs (e.g., explore–exploit ratios) that remain static throughout execution. While effective in some settings, these approaches often fail to adapt across tasks, or even within the same task as the search space changes over time. We introduce EvoX, an adaptive evolution method that optimizes its own evolution process. EvoX jointly evolves candidate solutions and the search strategies used to generate them, continuously updating how prior solutions are selected and varied based on progress. This enables the system to dynamically shift between different strategies during the optimization process. Across nearly 200 real-world optimization tasks, EvoX consistently outperforms prior open frameworks, including OpenEvolve, ShinkaEvolve, and GEPA. It achieves the best open-source performance on Frontier-CS (+34\% median over the best AI solutions across 172 problems), matches or surpasses AlphaEvolve and human-designed state-of-the-art solutions on multiple benchmarks, and does so at more than 3$\times$ lower cost on average compared to existing open systems and Claude Code.