Human-based evolutionary computation: Difference between revisions

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'''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:

'''Human-based evolutionary computation''' (HBEC) is a set of [[evolutionary computation]] techniques that rely on human innovation.

==Classes and examples==

'''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:

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| bgcolor=cornsilk | Recombination

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| bgcolor=cornsilk | Human-based selection dtrategystrategy

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

===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 (studies by Kosorukoff<ref name="kosorukoff2000">{{cite book |last1=Kosorukoff |first1=A. |title=2001 IEEE International Conference on Systems, 2000;Man and Cybernetics. E-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236) |chapter=Human based genetic algorithm |date=2001 |volume=5 |pages=3464–3469 |doi=10.1109/ICSMC.2001.972056|isbn=0-7803-7087-2 |s2cid=13839604 }}</ref> and Gentry,.<ref name="gentry2005">{{cite book |last1=Gentry |first1=Craig |last2=Ramzan |first2=Zulfikar |last3=Stubblebine |first3=Stuart |title=Proceedings of the 6th ACM conference on Electronic commerce |chapter=Secure distributed human computation |date=2005) |pages=155–164 |doi=10.1145/1064009.1064026|isbn=1595930493 |s2cid=56469 }}</ref>

==Examples==

===Human-based selection===

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 alredyalready 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 retractdetract 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.

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).

A better example of a human-based selection system is [[Stumbleupon]]. In Stumbleupon, users first experience the content (stumble upon it), and can then submit their preference by pressing a thumb-up or thumb-down button. Because the user doesn't see the number of votes given to the site by previous users, Stumbleupon can collect a relatively unbiased set of user preferences, and thus evaluate content much more precisely.

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.

===Human-based evolution strategy===

In this context and maybe generally, the Wikipedia software is the best illustration of a working human-based evolution strategy wherein the (targeted) evolution of any given page comprises the fine tuning of the knowledge base of such information that relates to that page.<ref name="leuf">{{cite book |last1=Leuf |first1=Bo |title=The Wiki way : quick collaboration on the Web |date=2001 |publisher=Addison-Wesley |___location=Boston |isbn=020171499X}}</ref> Traditional [[evolution strategy]] has three operators: initialization, mutation, and selection. In Wikipediathe case of Wikipedia, the initialization operator is a page creation, the mutation operator is an incremental page editediting. The selection operator is less salient. It is provided by the revision history and the ability to select among all previous revisions via a 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 previosprevious 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 waslacked lackinga selection operation and hence it couldn't effectively support content evolution. The addition of revision history and the rise of large wiki-supported communities coincide in time. From an evolutionary computation point of view, this is not surprising: without a selection operation the content would undergo an aimless [[genetic drift]] and would unlikely to be useful to anyone. That is what many people expectexpected from Wikipedia at theits very beginninginception. However, with a selection operation, the utility of the content havehas 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 algorithm]]s). 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.

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

== See also ==

Human-based genetic algorithm (HBGA) provides means for human-based recombination operation (a distinctive feature of [[genetic algorithm]]s). 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.

* {{annotated link|Incrementalism}}

* {{annotated link|Interactive evolutionary computation}}

==References==

{{reflist}}

{{Evolutionary computation}}

# 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]

[[Category:Human-based computation]]