Human-based evolutionary computation

Human-based evolutionary computation (HBEC) is a set of evolutionary computation techniques that rely on human innovation. Human-based evolutionary computation techniques can be classified into three more specific classes analogous to ones in evolutionary computation. There are three basic types of innovation: initialization, mutation, and recombination. Here is a table illustrating which type of human innovation are supported in different classes of HBEC:

All these three classes also have to implement selection, performed either by humans or by computers.

Examples

Human-based selection strategy

Human-based selection strategy 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 already 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 Stumbleupon, users first experience the content (stumble on it), 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 the previous users, Stumbleupon can collect relatively unbiased set of user preferences, and evaluate content much more precisely.

Human-based evolution strategy

In this context and maybe generally, the Wikipedia software is the best illustration of a working human-based evolution strategy. Traditional evolution strategy has three operators: initialization, mutation, and selection. In Wikipedia case, the initialization operator is a page creation, the mutation operator is an incremental page edit. The selection operator is less salient. It is provided by the revision history and the ability to select among all previous revisions via revert operation. If the page is vandalised and no longer a good fit to its title, a reader can easily go to the revision history and select one of the previous revisions that fits best (hopefully, the previous one). This selection feature is crucial to the success of the Wikipedia.

An interesting fact is that the original wiki software was created in 1995, but it took at least another six years for large wiki-based collaborative projects to appear. Why did it take so long? One explanation is that the original wiki software was lacking selection operation and hence it couldn't effectively support content evolution. The addition of revision history and rise of large wiki-supported communities coincide in time. From evolutionary computation point of view this is not surprising: without selection operation the content would undergo an aimless genetic drift and would unlikely to be useful to anyone. That is what many people expect from Wikipedia at the very beginning. However, with selection operation, utility of the content have a tendency to improve over time as beneficial changes accumulate. This is what actually happens on a large scale in Wikipedia.

Human-based genetic algorithm

The main article on this topic is Human-based genetic algorithm.

Human-based genetic algorithm (HBGA) provides means for human-based recombination operation (a distinctive feature of genetic algorithms). Recombination operator brings together highly fit parts of different solutions that evolved independently. This makes the evolutionary process more efficient. The HBGA methodology was derived in 1999-2000 from the analysis of [http://3form.com Free Knowledge Exchange] project that was launched in summer 1998 in Russia. Currently, several other projects implement the same model, the most popular is Yahoo! Answers launched in December 2005.

References

# Kosorukoff, A. (2000) Social classification structures. Optimal decision making in an organization, Genetic and Evolutionary Computation Conference, GECCO-2000, Late breaking papers, 175--178 [http://www.ews.uiuc.edu/~kosoruko/pub/classtre.pdf online]
# Kosorukoff, A. (2000) Human-based genetic algorithm [http://www.geocities.com/alex+kosorukoff/hbga/hbga.html online]
# Cunningham, Ward and Leuf, Bo (2001): The Wiki Way. Quick Collaboration on the Web. Addison-Wesley, ISBN 0-201-71499-X.
# Kosorukoff, A (2001), Human-based Genetic Algorithm. IEEE Transactions on Systems, Man, and Cybernetics, SMC-2001, 3464-3469
# Kosorukoff, A, Goldberg D. E. (2002), Evolutionary computation as a form of organization, Proceedings of Genetic and Evolutionary Computation Conference, GECCO-2002, pp 965-972
# Gentry, C et al (2005) Secure Distributed Human Computation In Ninth International Conference on Financial Cryptography and Data Security FC'2005 [http://fc05.ifca.ai/p26.pdf online]