Lawrence was referring to a class of optimization algorithm that does not adapt to the data. In the terminology of neural networks, it is only inference, not training. It only samples the function stochastically, and tries to walk downhill, but there is no feedback loop to improve the speed of the walk, there is only knowledge of how good the samples are.
There are improvements within the GA field that try to approximate gradients or do other trickery to create a "feedback loop to improve the speed of the walk" while still being fundementally not much more than the traditional evolution loop. This is mostly nice in fields like Reinforcement Learning where computing gradients can be difficult.
GAs can be quite useful in situations where there are tons of very bad local minima/maxima and you desire the global minima/maxima. Unfortunately, neural networks have lots of really great local minima/maxima and the state space is so large that you'll likely never get the global minima/maxima (and you wouldn't know it if you did). This is why "Neuroevolution" of neural network weights hasn't really caught on.
I think the author is referring to the possibility of using a well defined fitness function rather than a vast array of training data.
One of the beautiful aspects of Evolutionary Computation is how you can use it as a generative mechanism. A fairly simple fitness function can produce complex and intriguing outputs.
