This paper introduces AlphaEvolve, a system designed to automate the discovery of advanced algorithms by leveraging large language models (LLMs) within an evolutionary framework. The system works by taking a user-defined problem and evaluation criteria, then iteratively generating and improving code solutions through an evolutionary process powered by LLM ensembles. AlphaEvolve has successfully applied this method to solve complex open problems in areas like matrix multiplication and various fields of mathematics, often surpassing existing state-of-the-art results. It also demonstrates practical utility by optimizing components within Google's computing infrastructure. The research highlights the effectiveness of combining evolutionary algorithms with the capabilities of modern LLMs for scientific and algorithmic discovery, particularly in problems that allow for automated evaluation.