I started attending the meetings of his graduate seminar. One of his students, Reiko Tanese, was working on a thesis on GAs and had some rather puzzling results. Stephanie Forrest and I got interested and worked together to figure out what was going on with these results. Use a ranking or tournament selection scheme rather than fitness-proportionate selection, which tends to produce too much convergence too quickly. What other paradigms in AI do you think GAs work in best with.
GAs also seem quite promising for various molecular biology applications, such as molecular design.
I don't know much about recent results using GAs to evolve neural networks but know that some people have had success in that area. I think mathematical genetics approaches might give some insight as to why GAs behave the ways they do. For example, van Nimwegen, Crutchfield, and collaborators at the Santa Fe Institute have used approaches similar to those in mathematical genetics to explain why GAs sometimes show punctuated equilibrium-type behavior. I would hope that mathematical genetics approaches will yield some insights into questions such as when and why crossover is useful, and when and why coevolutionary approaches are useful. Mark Feldman and colleagues at Stanford have already made some progress on the first question.