Other people are implying that named algorithms, especially those named after someone famous, are somehow more special/difficult. Sometimes they are, but often they aren't. "Linear search" for instance is the name given to the easy idea of: iterate through an array until the value == search value or you hit the end. Most candidates could do this, though they might sweat a bit if you said "find x by implementing linear search" as the name for such a straightforward idea might not be known. They might have trouble writing proofs about its properties, which is where I imagine a lot of supposed scariness comes from. (i.e. "This approach looks correct but I haven't proved it yet." Academics aren't satisfied with just a test suite, or "looking at it".)

My favorite named-after-a-person one like this is Dijkstra's algorithm, which he claimed to have come up with in 20 minutes on the back of a napkin. If we suppose the average professional engineer is at most 3x slower/less brilliant than Dijkstra, it's not that unreasonable to imagine someone could reproduce the design on a whiteboard in a full hour...

Of course I don't buy that assumption, nor do I think it's a good problem or good idea to have as an interview filter even if it was true. (While I enjoy the occasional programming puzzle, I hate that they're lazily used to evaluate people in interviews so at least I avoid ever giving pure algorithm puzzles for interviews.) Nevertheless I agree with Mark that it's not "basically impossible" to come up with a good algorithm for many classes of algorithms and problems. I do wonder though how many people who could reinvent tortoise+hare without seeing it explicitly before would then be able to reinvent the teleporting turtle optimization right after.