Deep Neuroevolution is a term from the fields of Artificial Intelligence, Automation, and Industry 4.0. It describes an innovative process in which the methods of evolution - i.e. trying out, developing further, and selecting the best solutions - are used to automatically improve artificial neural networks.
Instead of being programmed by humans, algorithms in deep neuroevolution develop themselves independently. This works similarly to nature: different versions of a program compete against each other, and the most successful ones are used for the next round and further optimised.
A vivid example: In a factory, robots are intended to take on complex tasks, such as precisely assembling various parts. Thanks to Deep Neuroevolution, the robots learn independently over time how to optimise their movements and processes, thus mastering tasks that could previously only be done by humans.
Deep neuroevolution makes artificial intelligence more flexible and particularly strong when a lot of trial and error is required. This allows it to automate processes that were previously considered too complicated and opens up new possibilities for modern industry.
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