Human-based computation
In computer science, human-based computation is a technique when a computational process performs its function via outsourcing certain steps to humans (Kosorukoff, 2001). This approach leverages differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human-computer interaction.
In traditional computation, a human employs a computer to solve a problem: a human provides a formalized problem description to a computer, and receives a solution to interpret. In human-based computation, the roles are often reversed: the computer asks a person or a large number of people to solve a problem, then collects, interprets, and integrates their solutions.

# Early work
Early work implementing this concept was made by Victor Johnston and Karl Sims. They used human evaluation in an evolutionary computation (EC), by asking many humans to, in essence, be the fitness function for an evolutionary algorithm using their visual perception and aesthetic judgments  (Caldwell and Johnston, 1991; Sims, 1991). As a result, their programs could evolve beautiful faces and pieces of art. These programs effectively reversed the common interaction between computers and humans. In these programs, the computer is no longer an agent of its user, but instead, a coordinator aggregating efforts of many human evaluators. These and other similar research efforts were generalized as interactive evolutionary computation or aestetic selection, however the full potential of the technique was not fully explored as the role of humans was limited to evaluating solutions.
Human-based genetic algorithm (HBGA) is the EC method that allowed human participation in multiple different roles. In HBGA, humans are not limited to the role of evaluator, but can perform a diverse set of functions. In particular, they can contribute their innovative solutions into the evolutionary process, make incremental changes to existing  solutions, and perform intelligent recombination. In short, HBGA outsources to humans all operations of a typical genetic algorithm and can be viewed as a form of social organization coordinated by a computer program (Kosorukoff and Goldberg, 2002). As a result of outsourcing, HBGA can process the representations for which there is no computational innovation operators available, for example, natural languages. Thus, HBGA obviated the need for a fixed representational scheme that was a limiting factor of both standard and interactive EC.

# Classes of human-based computation
Human-based computation methods combine computers and humans in different roles. Kosorukoff (2000) proposed a way to describe division of labor in computation, that groups human-based methods into three classes. The following table uses the evolutionary computation model to describe four classes of computation, three of which rely on humans in some role. For each class, a representative example is shown. The classification is in terms of the roles (innovation or selection) performed in each case by humans and computational processes. This table is a slice of three-dimensional table. The third dimension defines if the organizational function is performed by humans or a computer. Here it is assumed to be performed by a computer.
Classes of human-based computation from this table can be referred by two-letter abbreviations: HC, CH, HH. Here the first letter identifies the type of agents performing innovation, the second letter specifies the type of selection agents. In some implementations (wiki is the most common example), human-based selection functionality might be limited, it can be shown with small h.

# Methods of human-based computation
- (HC) Darwin (Vyssotsky, Morris, McIlroy, 1961) and Core War (Jones, Dewdney 1984) These are games where several programs written by people compete in a tournament (computational simulation) in which fittest programs will survive. Authors of the programs copy, modify, and recombine successful strategies to improve their chances of winning.
- (CH) Interactive EC (Caldwell and Johnston, 1991; Sims, 1991) IEC enables the user to create an abstract drawing only by selecting his/her favorite images, so human only performs fitness computation and software performs innovative role.  Simulated breeding style introduces no explicit fitness, just selection, which is easier for humans.
- (HH2) Wiki (Cunningham, 1995) enabled editing the web content by multiple users, i.e. supported two types of human-based innovation (contributing new page and its incremental edits). However, the selection mechanism was absent until 2002, when wiki has been augmented with a revision history allowing for reversing of unhelpful changes. This provided means for selection among several versions of the same page and turned wiki into a tool supporting collaborative content evolution (would be classified as human-based evolution strategy in EC terms).
- (HH3) Human-based genetic algorithm (Kosorukoff, 1998) uses both human-based selection and three types of human-based innovation (contributing new content, mutation, and recombination). Thus, all operators of a typical genetic algorithm are outsourced to humans (hence the origin of human-based). Examples include the 3form Free Knowledge Exchange (website), Knowledge iN, and Yahoo! Answers, all of which implement some form of collaborative problem-solving.
- (HH1) Social search applications accept contributions from users and attempt to use human evaluation to select the fittest contributions that get to the top of the list. These use one type of human-based innovation. Early work was done in the context of HBGA. Digg and Reddit are recently popular examples.
- (HC) Computerized tests. A computer generates a problem and presents it to evaluate a user. For example CAPTCHA, a reverse Turing test, tells human users from computer programs by presenting a problem that is supposedly easy for a human and difficult for a computer (Suggested by Moni Naor, 1996). Unfortunately, the results obtained from humans are usually discarded. This makes it an example of wasting human computation.
- (HC) Interactive online guessing games: These are programs that extract knowledge from people in an entertaining way (Burgener, 1999; von Ahn 2003).

# Incentives to participation
In different human-based computation projects people are motivated by one or more of the following.
- Receiving a fair share of the result
- Direct monetary compensation (e.g. in Amazon's Mechanical turk)
- Desire to diversify their activity (e.g. "people aren't asked in their daily lives to be creative" )
- Esthetic satisfaction
- Curiosity, desire to test if it works
- Volunteerism, desire to support a cause of the project
- Reciprocity, exchange, mutual help
- Competitive spirit of a game
- Desire to communicate and share knowledge
- Desire to share a user innovation to see if someone else can improve on it
- Desire to game the system and influence the final result
Many projects had explored various combinations of these incentives. See more information about motivation of participants in these projects in Kosorukoff (2000) and 
von Hippel (2005).

# Human-based computation as a form of social organization
Viewed as a form of social organization, human-based computation often surprisingly turns out to be more robust and productive than traditional organizations (Kosorukoff and Goldberg, 2002). The latter depend on obligations to maintain their more or less fixed structure, be functional and stable. Each of them is similar to a carefully designed mechanism with humans as its parts. However, this limits the freedom of their human employees and subjects them to various kinds of stresses. Most people, unlike mechanical parts, find it difficult to adapt to some fixed roles that best fit the organization. Evolutionary human-computation projects offer a natural solution to this problem. They adapt organizational structure to human spontaneity, accommodate human mistakes and creativity, and utilize both in a constructive way. This leaves their participants free from obligations without endangering the functionality of the whole, making people happier. There are still some challenging research problems that need to be solved before we can realize the full potential of this idea.