The very presence of the "internal heuristic" that you mention is usually enough to make the algorithm greedy.
Otherwise, thanks for being nice, and try to take less offense at what people say on the Internet.
The very presence of the "internal heuristic" that you mention is usually enough to make the algorithm greedy.
Otherwise, thanks for being nice, and try to take less offense at what people say on the Internet.
That is most likely a wrong assumption. Greedy algorithms usually do not find globally optimal solutions, yet the people solving these problems are finding -the- solution. That is enough to suggest that the heuristic people are using is much more complex than merely choosing local optimums.
No one is wrong or right. It just depends on what you believe users are doing. One thing is certain: users use "local search" techniques as opposed to "systematic" search. The salesman problem describes this: the algorithm operates using a single current node and moves only to neighbors of that node.
However, depending on the "local search" algorithm used, those may be greedy or not: - "Hill Climbing" is greedy. The algorithm is affected by local maxima, ridges and plateaux. - "Simulated annealing" is not greedy. It uses "gradient descent" and does not always pick the "best" neighbors.
I believe the heuristics used by the users: - are not very precise ("Well, it is better, right?") - may change over time ("Oh, look at this!") That's why, I think it is not a "greedy" algorithm with deterministic results, but it is NOT complete anyway (hence, the clear process).
I can't wait for you guys to kick my butt :) ps: I'm a business major...