Reddit reviews Introduction to Operations Research with Student Access Card

Genetic algorithms (GAs), the most popular type of evolutionary algorithm, have attracted a lot of attention in the past. They're generally used to attack NP-hard problems, which basically means that the problems don't have a fast way of directly solving them and you'll have to settle for an approximation for serious problems. When it comes to solving these types of problems, you can imagine a large space that needs searching in some manner, where the dimensionality is usually very high. GAs work by combining information from candidate solutions in the space. Combining solutions can be slow and there's usually no reason that combining solutions the way GAs do should be very useful. Even worse, depending on the type of problem you're attacking and the way you encode solutions, the new candidate solution might not even be viable in the space.

I took a course in optimization where the book pretty much flat out said that GAs aren't worth it. I've spent some time working with these things and I found that, for my problems, implementing a local search component made it run faster, pretty much in proportion to how much I integrated the local search. I think GAs are really cool conceptually and people should know how they work, but I would not reach for one to solve a real problem.

From the GA Wikipedia page: "The question of which, if any, problems are suited to genetic algorithms (in the sense that such algorithms are better than others) is open and controversial."