This chapter establishes the two main algorithms used to compare model-based optimization with instance-based optimization. The first algorithm, SEESAW, represents a more complex, model-based AI search algorithm limited to the COCOMO ontology. SEESAW is compared to five other model-based approaches: BEAM, A-star, Simulated Annealing, MaxWalkSat, and ISAMP. From this comparison, SEESAW is chosen as the top performer.

Next, the {\W} algorithm is introduced. {\W} represents our implementation of instance-based reasoning. Because no other known algorithms extend instance-based reasoning to recommendation, no other algorithms directly compare to {\W}. Instead, the next chapter includes an experiment comparing the performance between {\W} and SEESAW.



