Evolutionary Algorithm Outperforms Deep-Learning Machines at Video Games


With all the excitement over neural networks and deep-learning techniques, it’s easy to imagine that the world of computer science consists of little else. Neural networks, after all, have begun to outperform humans in tasks such as object and face recognition and in games such as chess, Go, and various arcade video games.

These networks are based on the way the human brain works. Nothing could have more potential than that, right?

Not quite. An entirely different type of computing has the potential to be significantly more powerful than neural networks and deep learning. This technique is based on the process that created the human brain—evolution. In other words, a sequence of iterative change and selection that produced the most complex and capable machines known to humankind—the eye, the wing, the brain, and so on. The power of evolution is a wonder to behold. …

Evolutionary computing works in an entirely different way than neural networks. The goal is to create computer code that solves a specific problem using an approach that is somewhat counterintuitive.

The conventional way to create code is to write it from first principles with a specific goal in mind. 

Evolutionary computing uses a different approach. It starts with code generated entirely at random. And not just one version of it, but lots of versions, sometimes hundreds of thousands of randomly assembled pieces of code.

Read more at MIT Technology Review