Evolutionary Algorithm – The Surprising and Incredibly Useful Alternative to Neural Networks
- The evolutionary algorithm technique could significantly change the way we build deep learning models
- It has been around for a number of years and the latest research has been done by researchers from the University of Toulouse
- Their algorithm outperformed deep learning systems in Atari games, and did so in a far quicker time
Neural networks have become the be all and end all of all machine learning models. No matter which research blog you read about, DeepMind, Google AI, Facebook’s FAIR, etc., most of the latest research has neural networks at the core of the system.
From facial recognition and object detection to beating humans in board and video games, neural networks have developed an aura and power of their own. The concept has been around for decades, but has gained massive popularity in recent years thanks to advanced in technology and hardware. These neural nets are essentially based on how our brain works.
But a new type of algorithm, called Evolutionary Algorithm, has been developed that could significantly change the way we build and design deep learning models. Instead of trying to map the neurons like in a human brain, this approach is based on evolution – the process that has shaped the human brain itself. This evolutionary algorithm has been used to beat deep learning powered machines in various Atari games.
How does it work?
The evolutionary algorithm approach begins with generating code at a completely random rate (tons of versions of code actually). These code pieces are then tested to check whether the intended goal has been achieved. As you can imagine, most of the code pieces are scrappy and make no sense because of their random nature.
But eventually some pieces of code are found that are better than the rest. These pieces are then used to reproduce a new generation of code (which is not identical to the original code because that would defeat the purpose). As new code is generated, it is continuously tested and this process keeps repeating until such a code is found that is better than anything else at solving the problem. Can you now understand how this relates to the evolution of the human brain?
The algorithm outperformed deep learning systems by a comfortable margin. The best part? It did so in a much quicker fashion than any deep learning system there!
Read more about this algorithm in MIT’s Technology Review article and also ensure you read the highly detailed research paper. This paper was published by Dennis Wilson and his colleague at the University of Toulouse.
Our take on this
This evolutionary approach has been around for a while but due to the advancements in deep learning, it has taken a back seat. This research has already brought some attention to it. Apart from taking less training time, the code is fairly easy to interpret because the evolved approach means smaller code blocks. And interpretability is a MAJOR issue these days.
Are data scientists working on deep learning missing out on this technique? This research certainly puts the evolutionary algorithm right in the middle of the debate. It’s definitely worth checking out.
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