I found some months ago two very interesting audio presentations from David Fogel and other from David E. Goldberg. According to Fogel (hear here the presentation or download it), what works best is the synergistic effect obtained by combining simulated evolutionary learning with human learning. As an example of this latter approach he tells the story of Blondie24, a checkers program supplied with only minimal information that was able to reach high levels of expertise thanks to the application of genetic algorithms. Fogel looks at other real-world applications in industry, medicine, and defense, as well as speculating on the future capabilities offered by these combined learning mechanisms. It is a nice example of evolutionary computation application.
Goldberg's presentation is about the evolution of genetic algorithms through the 1990s, when GAs had their Warhol 15 minutes of fame, and where they are today. It is an interesting overview of genetic algorithms (although I disagree on some parts of his presentation), why they did not solve some classes of problems (maybe this paper here and this other here can help to understand that why), the results got by the first generation of genetic algorithms and, of course, the results obtained through new generation GAs, such as hBOA (Hierarchical Bayesian Optimization Algorithm). He also talks on the comparison made between a Simple Genetic Algorithm (or SGA) and hBOA on an antenna design problem. There is, of course, Goldberg's famous quote which he uses to ask why when the subject is conceptual machines, such as genetic algorithms, persons often want to see proof and rigor, while other machines, such as airplanes and toasters, are frequently used and persons do not ask for rigor and proof, here you are the quote:
"Nobody has ever formally proven that an airplane flies, but we all entrust our lives to them/it."
Yes, I agree that no one has ever proven mathematically/formally that an airplane can fly (or a toaster can toast), but an airplane (or toaster, fan, towel, shoes, etc) is not an algorithm! When we talk about evolutionary algorithms, we are also talking about algorithms (I guess it is clear) and we cannot disconnect an algorithm from its natural features, such as convergence proof and complexity analysis. If we disconnect them, the situation tends to get worse, because we need to consider those inherent features/characteristics to make a rigorous analysis of the algorithms' behaviour (it is valid for genetic algorithms too). Inevitably, that task needs rigor and proof. I suppose that would be difficult to convince people, mainly computer scientists, to use an algorithm which does not have all those stuffs I said. (If you want to read a more detailed version of that quote, please, see here.)