The Set Cover problem (SCP) and Set Packing problem (SPP) are standard NP-hard combinatorial optimization problems. Their decision problem versions are shown to be NP-Complete in Karp's 1972 paper. We specify a rough guide to constructing approximation heuristics that may have widespread applications and apply it to devise greedy approximation algorithms for SCP and SPP, where the selection heuristic is a variation of that in the standard greedy approximation algorithm. Our technique involves assigning to each input set a valuation and then selecting, in each round, the set whose valuation is highest. We prove that the technique we use for determining a valuation of the input sets yields a unique value for all Set Cover instances. For both SCP and SPP we give experimental evidence that the valuations we specify are unique and can be computed to high precision quickly by an iterative algorithm. Others have experimented with testing the observed approximation ratio of various algorithms over a variety of randomly generated instances, and we have extensive experimental evidence to show the quality of the new algorithm relative to greedy heuristics in common use. Our algorithms are somewhat more computationally intensive than the standard heuristics, though they are still practical for large instances. We discuss some ways to speed up our algorithms that do not significantly distort their effectiveness in practice on random instances.