Solve harder problems with AlphaEvolve, now available to everyone on Google Cloud

Traditional coding methods often cannot explore all the possible algorithms and implementations because the search space is simply too vast. To help, we introduced AlphaEvolve last year in private preview - an agent to help you design better algorithms on Google Cloud. What's new: Today, AlphaEvolve is generally available (GA) on Gemini Enterprise Agent Platform .

AlphaEvolve is a code optimization and discovery agent built on top of Gemini that helps solve the hardest algorithmic problems for your business and research. It has been tested in diverse domains like logistics, semiconductors, genomics, high performance computing, and financial services during our early access program. It systematically explores the search space to find solutions optimized for your problem.

Deploying AlphaEvolve within your environment follows a structured four-step process designed to move from initial problem definition to fully optimized production code: Define: Provide a baseline seed algorithm and problem definition, together with background knowledge that provides context about the problem you want to solve. Measure: Establish a scoring function to objectively score candidate programs on one or more metrics important for your problems such as correctness, performance, and operational constraints. Optimize: Use AlphaEvolve's agentic harness to generate optimized code.

Apply: Deploy the resulting, highly optimized algorithm directly into your production workloads and infrastructure. In this post, we'll share how organizations are already seeing impact with AlphaEvolve and how you can get started. How organizations are using AlphaEvolve AlphaEvolve has grown from a research project into a key tool we use at Google.

Now, some of the world's most innovative organizations are using it to solve their algorithmic problems, too. BASF: Building a digital twin to optimize global supply chains "We had several attempts to build a digital twin for our complex supply network using deterministic models, and all of them failed. By using AlphaEvolve, we can now not only map the complex network based on system data, but at the same time understand and copy the human decisions that drive our daily operations.

This gives us a highly accurate and easy to maintain data driven digital twin of the entire network. Goetz Krabbe, Vice President for Global Supply Chain, BASF . Visit the blog to read more how BASF used AlphaEvolve to improve their existing planning and forecasting models by over 80%.

Coolblue: Optimizing e-commerce demand forecasting "Coolblue data scientists used AlphaEvolve to directly optimize their 28-day demand forecasting pipeline, focusing on automated feature engineering, target preprocessing, and model selection. In just a few (200) iterations, AlphaEvolve improved our production forecast (by reducing WMAPE over the existing solution) by over 5%. These gains were achieved through improved feature engineering, an ensemble of different regression models, and better target preprocessing proposed and validated by AlphaEvolve.

To ensure sufficient stock availability, it is crucial that the demand forecast is accurate for both the short term (the first 7 days) and the longer horizon (the full 28 days). AlphaEvolve achieved this by using an evaluation metric that combines both periods, along with a strict penalty for under forecasting. AlphaEvolve has proven its ability to significantly improve bulk purchasing decisions and help us maintain optimal stock levels for the weeks ahead.