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.