The Objective Function for this application is all the contraints used
to match students and dorm rooms. The domain is all the students in
beds. A single data point, or state, is the assignment of each
student to a bed. 
maps from a state to a non-negative integer
score, where a zero score (if you could get there) would be `perfect'
with no errors, and everything else is worse. Starting at some state,
we try to improve it by moving to nearby states. By switching a
randomly chosen student with the occupant of a randomly chosen bed,
while all else stays constant, we can reach a nearby state. Each
state has some four million possible neighbors, since any of almost
2,000 students can be moved to any of 2,000 beds. To better illustrate
the concepts of neighborhood and landscape, we present
graphs showing a single data point from both random and optimized
configurations.