Summary

Conduct randomized, parallel, hill-climbing search through $H$.
Approach learning as optimization problem (optimize fitness).
GAs are most successful when the hypothesis are complex and the object to be optimized is an indirect function of the hypothesis (e.g., the set of rules that controls a robot).
GP is a variant of GA and has been successful at robot control and recognizing objects in visual scenes.
The art, as usual, lies in choosing the representation.