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

AlphaEvolve is available for everyone | Google Cloud Blog

AI & Machine Learning

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

July 10, 2026

##### Anant Nawalgaria

Group AI Product Manager & Engineer, Google

##### Laurynas Tamulevičius

Staff AI Software Engineer, Google

Many of the most challenging and valuable problems in the world are related to optimization. Now, AI is now making these problems tractable. If you've ever tried to design a microchip, plan a delivery network, or optimize a training architecture for a large AI model , you know how hard it is to find the most optimized code. 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." — Dr. 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.” — Cas Ruger, Data Scientist at Coolblue .

FM Logistic: Optimizing warehouse routing " Through our partnership with Google Cloud and the implementation of AlphaEvolve and Gemini, we further optimized our routing approach for fast-moving operations. The 10.4% improvement was achieved on top of an already highly optimized baseline, where further gains are typically hard to come by. This translates directly to faster fulfillment, improved working conditions for our teams, and reduced wear on our fleet ." — Rodolphe Bey , Group CIO at FM Logistic .

Visit the blog and website to read more about how FM Logistic used AlphaEvolve to improve warehouse routing by 10.4%, saving over 15,000 km in staff travel.

Infineon: Optimizing chip design " Our initial experiments with AlphaEvolve have been very positive, demonstrating its potential to transform the chip design lifecycle. We see a clear potential for it to contribute to multiple stages of development, including areas like Surrogate modelling." — Michael Kollig, CIO, Infineon .

JetBrains: Accelerating IDE performance " AlphaEvolve can change how we approach complex performance work. It turns optimizations that were once too time-consuming to explore into candidates we can test routinely. Engineers still own the benchmark, review, and release decision. The search space is what gets smaller. " — Dmitrii Batkovich, Director of Engineering, JetBrains .

Visit the blog to read more about how Jetbrains used AlphaEvolve to improve their IDE performance by over 15-20%.

Kinaxis: Improving optimization and forecasting systems "Kinaxis researchers have used AlphaEvolve to materially improve both the speed and quality of highly mature forecasting and optimization algorithms. In early testing, we achieved improvements of more than 22% in key forecasting accuracy metrics while reducing runtime by over 90% on benchmark datasets. As supply chains become increasingly complex and unpredictable, AlphaEvolve has the potential to help the world's largest organizations make faster, more informed decisions and adapt with greater confidence." — Gelu Ticala, Chief Technology Officer, Kinaxis

Visit the blog to read more about how Kinaxis used AlphaEvolve to achieve significant gains across their forecasting and runtime metrics.

Klarna: Doubling throughput while improving model quality " Klarna applied AlphaEvolve to one of their largest ML training pipelines and doubled throughput while improving model quality, all under the strict reproducibility constraints of regulated financial services. Over three weeks, the system explored nearly 6,000 candidate programs, discovering deep architectural rewrites no engineer would have tried. " — Klarna engineering team.

Visit the blog to read more about how Klarna used AlphaEvolve to double Training Speed and improve performance for their foundational models.

Kuro Games: Server-side Optimization "At Kuro Games, our guiding principle is that AI should not just make our work faster — it should make our work better. AlphaEvolve is a real-world validation of that principle. We applied it to a complex backend optimization challenge and saw substantial performance gains in specific server-side workloads. AlphaEvolve handles the kind of optimization work machines do best, so our engineers can focus on what only people can do: crafting great games." — Lin Chenchen Chief Technology Officer, Kuro Games

Oak Ridge National Laboratory: GPU kernel generation for exascale computing Under Google DeepMind’s Genesis Mission partnership with the Department of Energy to provide early-access to our AI for science tools.

“Oak Ridge National Laboratory (ORNL) recently partnered with Google to deploy AlphaEvolve on Frontier, the world’s first exascale supercomputer. The research team built a closed-loop evaluation architecture that bridges cloud-based large language model code generation with Frontier’s execution environment. The designed system optimizes mixed-precision GPU kernels—which requires complex, coupled decisions about memory, data layout, and hardware synchronization — by iteratively generating, compiling, running, and validating candidate programs, directly on the supercomputer's AMD GPUs. This executable search framework evaluates each proposed structural optimization against numerical accuracy rules.

“Our collaboration with Google's AlphaEvolve team gave us an early look at how evolutionary programming can be combined with leadership-class supercomputing. By running AlphaEvolve on Frontier, we explored a large number of optimization candidates in parallel, including novel implementation variants that helped us explore parts of the design space we might not have reached through manual optimization alone. This is an encouraging first step toward applying AI-assisted optimization to increasingly complex scientific software." — Oscar Hernandez Mendoza, PhD, Senior Computer Scientist, ORNL

Old Dominion University: Modeling biological aging mortality rates "The Qin Lab at Old Dominion University used AlphaEvolve to search the space of Python programs that model biological aging mortality rates, a problem in computational biogerontology where the governing equations span multiple empirical laws. Utilizing an HPC cluster in Google Cloud as a part of the ODU MonarchSphere initiative, AlphaEvolve – across approximately 500 evaluations – independently rediscovered the Kannisto logistic mortality model (a published result from the 1990s biogerontology literature) with no prior knowledge of that literature, improved the Emergent Aging Model composite fitness score by 19% through heterogeneous decay rate distributions, and demonstrated near-perfect Strehler-Mildvan correlation (0.949) via scale-free network topology with Laplacian spectral aging across approximately 500 evaluations. The central finding is that structurally diverse models all converge on the same empirical aging laws, providing evidence that Gompertz, Strehler-Mildvan, and Kannisto regularities are robust attractors of biological systems. The team plans to extend this work to multi-species datasets and to connect the evolved program structures to testable biological mechanisms.” — Dr. Hong Qin,Department of Computer Science, Old Dominion University

PacBio: Scaling accuracy and lowering costs in genomics "The solution the Google team discovered using AlphaEvolve unlocks meaningfully higher accuracy rates for our sequencing instruments. For researchers, this higher-quality data might enable the discovery of previously hidden disease-causing mutations." — Aaron Wenger Senior Director, PacBio .

Visit the blog to read more about how Pacbio used AlphaEvolve to improve DeepConsensus — a model developed by Google Research for correcting DNA sequencing errors — achieving a 30% reduction in variant detection errors.

Pebble: Optimizing serving performance on GPUs "Optimizing inference serving is an incredibly challenging problem because it is a multi-dimensional system design challenge that shifts dynamically between memory, compute, and hardware orchestration constraints. NVIDIA's AI Configurator latency model was severely bottlenecked by a single, static 0.8 empirical correction factor that applied uniformly to all workloads, and did not model FP8-vs-BF16 efficiency divergence, causing recommended configurations to drift away from the optimum. AlphaEvolve solved this by autonomously discovering GPU performance modeling formulations directly from our training prior. This Gemini-powered evolutionary approach drastically cut our model errors by more than delivering a 56% relative error reduction. We are excited to integrate this smoother, learned efficiency function and leverage AlphaEvolve to continuously map emerging hardware specifications without manual tuning." — Keval Shah Head of AI, Pebble

Qbraid: Advancing quantum computing "AlphaEvolve delivered a result on top of an encoding family we had already spent years refining. It searched a design space far too large to comb through by hand and handed back something we could read, verify, and understand. Systems like AlphaEvolve will meaningfully accelerate progress toward useful quantum computing." — Kenny Heitritter, Vice President of Research and Development at qBraid .

Visit the blog and paper to read more about how Qbraid used AlphaEvolve to find significantly more error efficient error-correcting codes for quantum chemistry.

Schrödinger: Shortening cycles for molecular simulations for drug discovery "AlphaEvolve allows us to explore larger chemical spaces faster and more efficiently than ever before. Faster MLFF inference carries real business impact, shortening R&D cycles in drug discovery, catalyst design, and materials development, and enabling companies to screen molecular candidates in days rather than months." — Gabriel Marques, ML Tech Lead, Schrödinger .

Visit the blog to read more about how Schröedinger used AlphaEvolve to quadruple the speed of molecular discovery.

Substrate: Accelerating runtime speed for semiconductor simulation “AlphaEvolve transformed the speed and efficiency of our computational lithography frameworks and, more impressively, demonstrated the potential of these models to design their future selves, all the way down to the atoms.” — James Proud, CEO, Substrate .

Visit the blog to read more about how Substrate applied AlphaEvolve to its computational lithography framework, achieving a multi-fold increase in runtime speed, enabling them to run significantly larger simulations of advanced semiconductors.

WPP: Cracking the code of campaign success " WPP faced a ceiling in predicting creative campaign performance, as their manual model optimizations yielded only marginal 1% accuracy gains despite significant time and effort. To overcome this challenge, WPP’s Research team utilized AlphaEvolve to autonomously propose, evaluate, and refine candidate model architectures rather than relying on slow manual experimentation. This agentic framework effectively bypassed their trial-and-error limits, successfully navigating complex, high-dimensional campaign data and class imbalances. As a result, WPP achieved a highly significant 5–10% (across different use cases) increase in both prediction accuracy and downstream recommendation scores, outperforming all previous baseline models including neural and fine-tuned Gemma models. " — Anastasios Tsourtis, Lead Data Scientist, WPP .

Visit the blog to read more about how WPP used AlphaEvolve to optimize machine learning models for digital marketing campaigns, delivering a 10% lift in prediction accuracy and up to a 7% boost in downstream recommendation scores.

Hardening our own infrastructure and scientific research

Beyond external deployments, Google has integrated AlphaEvolve as a core engine to scale its own state-of-the-art infrastructure. As detailed by Google DeepMind , AlphaEvolve has successfully optimized the silicon design of next-generation Tensor Processing Units (TPUs) with a highly efficient, counterintuitive circuit layout, refined Google Spanner’s Log-Structured Merge-tree compaction heuristics to reduce write amplification by 20%, and reduced software storage footprints by nearly 9% through new compiler optimization strategies. Additionally, the agent has made critical contributions to scientific research, boosting predictive accuracy across 20 natural disaster risk categories by 5%, and discovering quantum circuits with 10x lower error rates for running complex molecular simulations on Google's Willow quantum processor.

According to Pushmeet Kohli, Chief Scientist, Google Cloud & Vice President, Science at Google DeepMind, “AI is moving beyond acting as a productivity assistant that accelerates how we work to a discovery engine that expands what we can achieve. By autonomously navigating complex computational search spaces, tools like AlphaEvolve are helping researchers and engineers uncover breakthrough algorithms that augment traditional human intuition”.

Start evolving your codebase today

Getting started with AlphaEvolve requires only two core inputs on your end:

• Seed program: The initial algorithm written as code. You designate which segments of code are open to optimization and provide them to AlphaEvolve

• An evaluator: A deterministic client-side evaluation script that compiles, tests, and scores the mutated candidates, returning one or more scalar metrics for AlphaEvolve to maximize.

Your client-side runner queries the AlphaEvolve API to acquire mutated candidate solutions, runs them through your client-side evaluator (which can be running anywhere), and submits the scores back to AlphaEvolve which you sample from.

To use AlphaEvolve we recommend getting going through the documentation . After quickly setting up the AlphaEvolve API using the onboarding guide , we recommend starting going through the repository with the basic colab examples to understand how the AlphaEvolve heuristic works. For agentic workflows, you can easily get started using the AlphaEvolve Skill in your IDE of choice, such as Antigravity or Claude Code. For more complex experimentation, our best practices guide and advanced examples provide additional resources to run through detailed AlphaEvolve experiment workflows.

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