My PhD. is about Evolutionary Estrategies. More Precisely in Genetic Algorithms (GAs). I understand the GAs like a complex machine of optimization that knows nothing about the problem which it is trying to (re)solve.
This Particularity (detail) is very interesting, due to the fact that you can apply the GAs to different fields. For example, on the web you will find a lot of resources about GAs, such as Libraries of algorithms, examples of code, images and a lot of explanations.

The complex thing in a GA is not the programming part, as you can think, Is the way of how you define the problem that you want to optimize. This is usually a function called Fitness Function (FF). The correct set of your FF will determine the increase of the population’s level in each iteration. Therfore the effort must be put here.

The FF allows to evaluate each solution (individual) and to set who is who in the population, or in a different way, which of these solutions are the best and which are not.

Sometimes you can work with a really simple FF, like evaluating the length of a path, that you want to reduce. Besides, it can consists os a series of different equations that each individual must test. Logically, this process involve computational resources, it take some seconds or even minutes to achieve a correct evaluation.

In the case of one of my experiments, the evaluation consisted of determine how much energy a building spends in a specific weather. To achieve that I had to write a series of classes in C# (some of classes are built embedded in GC). Well a whole programme (with out interface, Console application) to allow me to transform the complete geometry in a different type of file for its thermal analysis in EnergyPlus.

This Video shows one of my first experiments in which I connect automatically (send and retrieve the data) GC and E+.