Google DeepMind promises to help you evolve your algos

AlphaEvolve may optimize your code in ways you hadn’t thought possible. Or not. Not is possible, too

Google's AI shop DeepMind has unveiled AlphaEvolve, its "evolutionary coding agent" powered by large language models to discover and optimize algorithms.

Computer algorithms are sets of instructions used to solve complex problems. AlphaEvolve is pitched as a useful tool for mathematicians, scientists, and engineers working on algorithmic tasks, ranging from abstract mathematical proofs to scheduling jobs across datacenters. It promises to evaluate the performance of code using automated metrics, then proposes improvements by evolving new versions of the algorithm.

For example, in an effort to improve matrix multiplication - a core operation in machine learning - AlphaEvolve discovered a new algorithm for multiplying 4×4 complex-valued matrices using just 48 scalar multiplications, surpassing Strassen’s 1969 result, Google explains.

Because AlphaEvolve focuses on code improvement and evaluation rather than representing hypotheses in natural language like Google's AI co-scientist system, hallucination is less of a concern.

Inside Google, researchers say AlphaEvolve has improved the efficiency of data center scheduling, chip design, and AI training. They also credit it with helping design faster matrix multiplication algorithms and generating new solutions to long-standing math problems.

"AlphaEvolve pairs the creative problem-solving capabilities of our Gemini models with automated evaluators that verify answers, and uses an evolutionary framework to improve upon the most promising ideas," the AlphaEvolve team explains in a blog post.

DeepMind's use of the term "evolve" makes the coding agent's technological process sound organic. The accompanying paper [PDF] also uses terms with biological associations: "AlphaEvolve extends a long tradition of research on evolutionary or genetic programming, where one repeatedly uses a set of mutation and crossover operators to evolve a pool of programs."

this is one more sign that neurosymbolic techniques that combine neural networks with ideas from classical AI, is the way of the future

Asked whether Google's description of agent is overly anthropomorphic, Gary Marcus, an AI expert, author, and critic, told The Register that the terminology is fair enough.

"The use of the term is fine, standard in that field and not unreasonable," he said. "It’s great to see DeepMind think outside the box of pure large language models, and this is one more sign that neurosymbolic techniques that combine neural networks with ideas from classical AI, is the way of the future."

Stuart Battersby, CTO of AI firm Chatterbox Labs, expressed optimism about AlphaEvolve's potential, while also emphasizing the need to keep security in mind during any AI deployment.

"The development of AI algorithms needs to happen at pace, and so it is great to see AlphaEvolve helping to automate this process," he told The Register. "This means that AI solutions not only get through the development cycle quicker, but hopefully produce better results too – it seems that the AlphaEvolve team have provided evidence of this."

Google has used AlphaEvolve to optimize the performance of its Borg compute cluster management system in its datacenters. According to the researchers, the coding agent proposed a heuristic function for online compute job scheduling that outperformed one running in production.

"This solution, now in production for over a year, continuously recovers, on average, 0.7 percent of Google’s worldwide compute resources," the researchers claim.

The DeepMind team also note that AlphaEvolve helped optimize matrix multiplication operations involved in the training of Google's Gemini model family by speeding up its Pallas kernel 23 percent, for a training time reduction of 1 percent.

To evaluate AlphaEvolve's utility, the DeepMind team gave it more than 50 open problems in mathematical analysis, geometry, combinatorics, and number theory.

"In roughly 75 percent of cases, it rediscovered state-of-the-art solutions, to the best of our knowledge," the researchers claim. "And in 20 percent of cases, AlphaEvolve improved the previously best known solutions, making progress on the corresponding open problems."

Google is planning to offer early access to academics. Those interested can apply here. ®