[ad_1]
That is the final a part of my collection of nature-inspired articles. Earlier, I had talked about algorithms impressed by genetics, swarm, bees, and ants. At this time, I’ll discuss wolves.
When a journal paper has a quotation rely spanning 5 figures, you realize there’s some severe enterprise happening. Gray Wolf Optimizer [1] (GWO) is one such instance.
Like Particle Swarm Optimization (PSO), Synthetic Bee Colony (ABC), and Ant Colony Optimization (ACO), GWO can be a meta-heuristic. Though there’s no mathematical ensures to the answer, it really works nicely in follow and doesn’t require any analytical data of the underlying drawback. This enables us to question from a ‘blackbox’, and easily make use to the noticed outcomes to refine our resolution.
As talked about in my ACO article, all these finally relate again to the basic idea of explore-exploit trade-off. Why, then, are there so many alternative meta-heuristics?
Firstly, it’s as a result of researchers need to publish papers. An excellent a part of their job entails exploring issues from totally different angles and sharing the methods during which their findings result in advantages over current approaches. (Or as some would say, publishing papers to justify their salaries and search promotions. However let’s not get there.)
Secondly, it’s as a result of ‘No Free Lunch’ theorem [2] which the authors of GWO themselves talked about. Whereas that theorem was particularly saying there’s no free lunch for optimization algorithms, I believe it’s honest to say that the identical is true for Knowledge Science typically. There isn’t any single final one-size-fits-all resolution, and we frequently need to attempt totally different approaches to see what works.
Due to this fact, let’s proceed so as to add yet one more meta-heuristic to our toolbox. As a result of it by no means hurts to have one other instrument which could turn out to be useful someday.
First, let’s think about a easy classification drawback on photographs. A intelligent method is to make use of pre-trained deep neural networks as characteristic extractors, to transform…
[ad_2]