Скачать или смотреть Differential Evolution is seriously underrated

You really should consider using Differential Evolution in your AI Solutions.

First time hearing about it? DE “ is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality”. It does so by, optimizing “a problem by maintaining a population of candidate solutions and creating new candidate solutions by combining existing ones according to its simple formulae, and then keeping whichever candidate solution has the best score or fitness on the optimization problem at hand”.

Simply put, Differential Evolution will go over each of the solutions. If it matches criterion (meets minimum score for eg.), it will be added to the list of candidate solutions. New solutions might be found by doing simple math operations on candidate solutions. When iterations are finished, we take the solution with the highest score (or whatever criterion we want).

Why use Differential Evolution. There are 4 reasons I can give you-

Range- Since it doesn’t evaluate the gradient at a point, IT DOESN’T NEED DIFFERENTIALABLE FUNCTIONS. This is not to be overlooked. For a function to be differentiable, it needs to have a derivative at every point over the domain. This requires a regular function, without bends, gaps, etc. DE doesn’t care about the nature of these functions. They can work well on continuous and discrete functions. DEs can thus be (and have been)used to optimize for many real-world problems with fantastic results.

Performance- Differential Evolution has one of the best bang for buck in the AI space. Many great innovations- such as the amazing One Pixel Attack- have leveraged DE for cost-effective exploration of the search space.

Adaptability: Papers have shown a vast array of techniques that can be bootstrapped into Differential Evolution to create a DE optimizer that excels at specific problems. This is a trend with Evolutionary Algorithms in general, which are tacked on as optimizers for other techniques.

Semi-Black Box when dealing with DNNs- DE is not a black-box algorithm. The functioning and process are very transparent. This makes it very good for tracing steps, and building on your system. It might require black-box feedback(probability labels)when dealing with Deep Neural Networks, but they can still provide great insight into your system.

I think Evolutionary/Swarm techniques are often overlooked in today's AI landscape. We'll do a deep-dive on them eventually. If you come across any interesting use-cases, please do share them.