Evolutionary computation is an important component within various fields such as artificial intelligence research, reinforcement learning, robotics, industrial automation and/or optimization, engineering design, etc. Considering the increasing computational demands and the dimensionalities of modern optimization problems, the requirement for scalable, re-usable, and practical evolutionary algorithm implementations has been growing. To address this requirement, we present EvoTorch: an evolutionary computation library designed to work with high-dimensional optimization problems, with GPU support and with high parallelization capabilities. EvoTorch is based on and seamlessly works with the PyTorch library, and therefore, allows the users to define their optimization problems using a well-known API.

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.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! The promise of evolutionary algorithms has been around for several years, offering organizations the elusive prospect of an advanced self-learning approach for artificial intelligence (AI). A key challenge, however, has been that few evolutionary algorithm technologies have been available under an open-source license. That is changing today: Switzerland-based AI vendor NNAISENSE announced the formal release of its EvoTorch open-source evolutionary algorithm technology.