In my experience (I worked on convergence analysis of some early GAs), genetic algorithms suffer from a combination of extremely easy implementation, a "cool" f

In my experience (I worked on convergence analysis of some early GAs), genetic algorithms suffer from a combination of extremely easy implementation, a "cool" factor that plagued neural-networks-as-classifiers research in the early 90s, and a belief that biological "analogies" would perform better than decades of research in stochastic optimization. Still, they have pedagogical value because they are easy to implement, and for many students, are their first real soup-to-nuts "AI" algorithm implementation. I've seen them serve as the gateway for students to get into CS research, but IMO aspects such as the last resort principle, no free lunch theorem, and principles of stochastic optimization are generally under-stressed, which leads to some abhorrent research papers along the lines of "Problem X using GAs".