The rapid rise of machine learning and artificial intelligence has resulted in a mass of complex computational and operational challenges that some engineers are trying to tackle with evolutionary algorithms, which work towards multiple optimization objectives concurrently. Industrial Al company NNAISENSE has developed an open-source platform which leverages evolutionary algorithms as the building blocks for cascading machine learning challenges, helping spur industry growth.

The platform, called EvoTorch, provides a software tool set that enables developers to experiment with evolutionary algorithms at any scale, without worrying about underlying details. The platform, built on the popular PyTorch and Ray packages, can create evolutionary algorithms that can be parallelized across CPUs or GPUs with little additional effort.

“EvoTorch was conceived about five years ago, when the idea came to us to apply evolutionary algorithms to industrial projects and address the intricate challenges associated with scaling.” said Dr. Timothy Atkinson, Research Scientist at NNAISENSE, in an interview with Design News.

“We approached EvoTorch as  researchers,” added Dr. Jonathan Masci, Co-Founder and Chief Scientist for Deep Learning at NNAISENSE. “This platform represents a crucial step forward for the evolutionary algorithm community by reducing the need for boilerplate code, allowing academics and developers to focus on solving problems at scale.”

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The key to the open-source platform is evolutionary algorithms, which function according to the principles of natural selection. These algorithms start with a population of random solutions that are evaluated for fitness (propensity to solve the problem). At each iteration, the fittest or most appropriate solutions reproduce resulting in an increasingly fit population that collectively adapts to solve the problem. From the final population of solutions, it is possible to select the one that best achieves the desired trade-off between multiple conflicting goals.

With machine learning becoming more important, evolutionary algorithms become an attractive solution to cascading challenges that accompany the increased complexity and size of automated processes. According to NNAISENSE’s Atkinson, evolutionary algorithms thrive on scale and are much more amenable to massive parallelization on modern hardware. This enables various industrial problems to be tackled with greater efficiency.

The software tool-set developed by NNAISENSE contains a library of algorithms, tools to define scalable implementations of problems that the developer is trying to solve, along with integration to well-known monitoring libraries which makes it easy to incorporate EvoTorch into existing workflows. Given its ever-expanding range of evolutionary algorithms and its intuitive interface, EvoTorch can also greatly simplify the job of academics and university students developing new algorithms.

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The company is backing the platform with an open-source machine learning community that gives developers the tools to scale up their designs quickly and easily. NNAISENSE plans to expand the algorithm feature set to meet developer needs for various applications.

Spencer Chin is a Senior Editor for Design News covering the electronics beat. He has many years of experience covering developments in components, semiconductors, subsystems, power, and other facets of electronics from both a business/supply-chain and technology perspective. He can be reached at [email protected].