The center of the research of our institute 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.

Every question that asks for a thing with a superlative feature is an optimization problem. Constructing the fastest car, finding the most profitable investment plan, finding a way to discover diseases as early and reliable as possible, scheduling your work so that you have the most spare time left for gaming – all of these are optimization tasks.

In this article, we will explore what an optimization problem in more detail, we will distinguish hard from easy problems and discuss what implications come from "hardness". We will discuss how exact and approximate algorithms tackle hard problems, give a slightly more formal definition of optimization problem, and list some more examples for optimization tasks. (Additionally, we also have a course on "Metaheuristic Optimization" and you can find all its slides here.)

The inspiration gleaned from observing nature has led to several important advances in the field of optimization. Still, it seems to me that currently 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.

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