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Human-based selection is a simplest human-based evolutionary computation procedure. It is used heavily today by websites outsourcing collection and selection of the content to humans (user-contributed content). Viewed as evolutionary computation, their mechanism supports two operations: initialization (when a user adds a new item) and selection (when a user expresses preference among items). The website software aggregates the preferences to compute the fitness of items so that it can promote the fittest items and discard the worst ones. Several methods of human-based selection were analytically compared in (Kosorukoff, 2000; Gentry, 2005).
Because the concept seems too simple, most of the websites implementing the idea can't avoid the common pitfall: [[informational cascade]] in soliciting human preference. For example, [[digg]]-style implementations, pervasive on the web, heavily bias subsequent human evaluations by prior ones by showing how many votes the items alredy have. This makes the aggregated evaluation depend on a very small initial sample of rarely independent evaluations. This encourages many people to game the system that might add to digg's popularity but retract from the quality of the featured results. It is too easy to submit evaluation in digg-style system based only on the content title, without reading the actual content supposed to be evaluated.
A better example of human-based selection system is [[Stumbleupon]]. In this implementation, users first experience the content, then can submit their preference by pressing a thumb-up or thumb-down button. Because user doesn't see the number of votes given to the site by previus users, Stumbleupon can collect relatively unbiased set of user preferences.
===Human-based evolution strategy===
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