Evolutionary Computation (EC)
Evolutionary Computation (EC) is the research field of metaheuristics that deals with population-based optimization. It comprises a wide range of methods, such as Evolutionary Algorithms (EAs) over Swarm Intelligence (SI).
Why you should use Evolutionary Algorithms to solve your optimization problems (and why not).
Some time ago, I discussed why global optimization with an Evolutionary Algorithm (EA) is not necessarily better than local search. Actually, I was asked “Why should I use an EA?” quite a few times. Thus, today, it is time to write down a few ideas about why and why not you may benefit from using an EA. I tried to be objective, which is not entirely easy since I work in that domain.
What is optimization?
The center of my research is optimization. But what is optimization? Basically, optimization is the art of making good decisions. It provides us with a set of tools, mostly from the areas of computer science and mathematics, which are applicable in virtually all fields ranging from business, industry, biology, physics, medicine, data mining, engineering, to even art.
Why research in Computational Intelligence should be less nature-inspired.
The inspiration gleaned from observing nature has led to several important advances in the field of optimization. Still, it seems to me that a lot of work is mainly based on such inspiration alone. This might divert attention away from practical and algorithmic concerns. As a result, there is a growing number of specialized terminologies used in the field of Evolutionary Computation (EC) and Swarm Intelligence (SI), which I consider as a problem for clarity in research. With this article, I would like to formulate my thoughts with the hope to contribute to a fruitful debate.