Using evolutionary algorithms in molecular biology? I am currently an MSc student studying molecular biology (on paper I'm a chemist) and soon I have to talk to my supervisor about my future, PhD research topic. Besides molecular biology I'm also interested in machine learning, mostly in evolutionary algorithms (evolutionary strategy alg.s, genetic alg.s) and Bayesian optimization, from both of which I have written multiple, working programs in C# at home. Due to the massive need for people trained in informatics I'd like to convince my supervisor to include this topic into my future research, especially as I have already acquired some basic skills in these areas. However I have only seen research examples that are purely computational and these didn't include any laboratory work. Can you provide me some insight into the use of these techniques in molecular biology and ways of connecting these with laboratory work?

There are countless really worthwhile ways to use evolutionary computation (EC) in molecular biology. The chief advantage of EC is that it can find solutions to problems that are too complicated, too nonlinear, etc., to solve by more direct methods. I've used genetic algorithms to evolve models, to find patterns in data, to design optical systems, you name it; and almost always a GA finds solutions I'd never find on my own. Sometimes it's a challenge to find a representation for the solution space that fits the problem well, or to find recombination/mutation operators that work well, but that's where human insight is important. Bottom line: if you can list some problems that fascinate you (or better, fascinate both you and your supervisor), I'd be happy to give you some concrete suggestions.