I saw a phenomenal talk in grad school about why genetic algorithms are generally not as good as stochastic hill climbing. Unfortunately, I can't track down an

I saw a phenomenal talk in grad school about why genetic algorithms are generally not as good as stochastic hill climbing. Unfortunately, I can't track down any references to it. The punch line was that they mathematically proved that genetic algorithms produce less optimal solutions than hill-climbing. So why are "genetic algorithms" so common in nature? Well, it turned out that genetic algorithms were better at producing solutions that were resilient to changing environmental conditions. They produced a less sharp peak at the solution they did find, so when a change occurred in the underlying conditions, the still had a reasonable chance of being at least ok.