As EVA2 is mainly about Evolutionary and Heuristic Optimization, some of the terms and notions are borrowed from the area and used in this document. As they may not be familiar to all who want to use the framework, we give a short summary here.
We aim at optimizing a target function without knowing much about it, and find a certain position in the search space which minimizes the function, called the solution. During search, we use a specific search strategy, the optimizer, which usually looks at several positions in parallel. Those are all potential solutions, because we don't know the real one yet. For the potential solutions we evaluate the target function. The value received is often called fitness in analogy to Darwin's Theory of Evolution, where ``the fitter ones survive''. For the same reason, potential solutions are sometimes called individuals, and the set of potential solutions stored by the optimizer at a time may be called the population. Many of the implemented optimization strategies employ operators in analogy to natural mutation, crossover and selection.
There is nothing mystical about that, and of course the analogy is often exaggerated. Evolutionary Optimization is an algorithmic tool that serves mostly technical purposes. That it works in a computer is by no means a sign that we fully understand natural evolution or can proof anything about it. This said, of course, we would never doubt that natural evolution in fact works.
This document will not explain in detail how the implemented optimizers work, as there is enough literature out there handling these topics. We refer to Sec. 6 for suggestions on further reading.