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 search strategies during the optimization process. Across nearly 200 real-world optimization tasks, EvoX outperforms existing AI-driven evolutionary methods including AlphaEvolve, OpenEvolve, GEPA, and ShinkaEvolve on the majority of tasks.

翻译：近期研究（如AlphaEvolve）表明，将基于大语言模型的优化与进化搜索相结合，能够有效提升跨领域程序、提示词及算法的性能。该范式通过复用已评估的解决方案来引导模型生成新的候选方案。关键在于，这种进化过程的有效性取决于搜索策略：即如何选择先验解并通过变异生成新候选解。然而，现有方法大多依赖固定搜索策略，其预设参数（如探索-利用比率）在整个执行过程中保持不变。虽然在某些场景下有效，但这些方法往往难以适应不同任务，甚至无法在同一任务中随搜索空间动态变化而调整。本文提出EvoX——一种能够优化自身进化过程的自适应进化方法。EvoX通过联合进化候选解及其生成策略，持续根据优化进度更新先验解的选择与变异机制，从而实现在优化过程中动态切换不同搜索策略。在近200个现实世界优化任务中，EvoX在大多数任务上超越了现有AI驱动的进化方法，包括AlphaEvolve、OpenEvolve、GEPA和ShinkaEvolve。