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Human-based Evolutionary Computation
Human-based evolutionary computation (HBEC) is a set of evolutionary computation techniques that rely on human innovation. Classes and examples 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. 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 expresse ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Evolutionary Computation
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection) and mutation. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm. Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Informational Cascade
An Information cascade or informational cascade is a phenomenon described in behavioral economics and network theory in which a number of people make the same decision in a sequential fashion. It is similar to, but distinct from herd behavior. An information cascade is generally accepted as a two-step process. For a cascade to begin an individual must encounter a scenario with a decision, typically a binary one. Second, outside factors can influence this decision (typically, through the observation of actions and their outcomes of other individuals in similar scenarios). The two-step process of an informational cascade can be broken down into five basic components: # There is a decision to be made – for example; whether to adopt a new technology, wear a new style of clothing, eat in a new restaurant, or support a particular political position # A limited action space exists (e.g. an adopt/reject decision) # People make the decision sequentially, and each person can observe the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Digg
Digg, stylized in lowercase as digg, is an American news aggregator with a curated front page, aiming to select stories specifically for the Internet audience such as science, trending political issues, and viral Internet issues. It was launched in its current form on July 31, 2012, with support for sharing content to other social platforms such as Twitter and Facebook. It formerly had been a popular social news website, allowing people to vote web content up or down, called ''digging'' and ''burying'', respectively. In 2012, Quantcast estimated Digg's monthly U.S. unique visits at 3.8 million. Digg's popularity prompted the creation of similar sites such as Reddit. In July 2008, the former company took part in advanced acquisition talks with Google for a reported $200 million price tag, but the deal ultimately fell through. After a controversial 2010 redesign and the departure of co-founders Jay Adelson and Kevin Rose, in July 2012 Digg was sold in three parts: the Digg brand ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Game The System
Gaming the system (also rigging, abusing, cheating, milking, playing, working, or breaking the system, or gaming or bending the rules) can be defined as using the rules and procedures meant to protect a system to, instead, manipulate the system for a desired outcome. According to James Rieley, a British advisor to CEOs and an author, structures in companies and organizations (both explicit and implicit policies and procedures, stated goals, and mental models) drive behaviors that are detrimental to long-term organizational success and stifle competition. For some, error is the essence of gaming the system, in which a gap in protocol allows for errant practices that lead to unintended results. Although the term generally carries negative connotations, gaming the system can be used for benign purposes in the undermining and dismantling of corrupt or oppressive organisations. History The first known documented use of the term "gaming the system" is in 1975. Examples Finance He ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stumbleupon
StumbleUpon was a discovery and advertisement engine (a form of web search engine) that pushed web content recommendations to its users. Its features allowed users to discover and rate Web pages, photos and videos that are personalized to their tastes and interests using peer-sourcing, social-networking and advertising (sponsored pages) principles. The service shut down in June 2018. Toolbar versions existed for Firefox, Google Chrome, Opera, Internet Explorer, and Safari. StumbleUpon also worked with some independent Mozilla-based browsers. Native mobile StumbleUpon apps existed for Windows, iOS, Android, and the Amazon Appstore. History StumbleUpon was founded in November 2001 by Garrett Camp, Geoff Smith, Justin LaFrance and Eric Boyd during Camp's time in graduate school at the University of Calgary. The idea of creating a company was established before the content: of the five or six ideas for products, StumbleUpon was chosen. Camp describes in a BBC interview the moment ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Evolution Strategy
In computer science, an evolution strategy (ES) is an optimization technique based on ideas of evolution. It belongs to the general class of evolutionary computation or artificial evolution methodologies. History The 'evolution strategy' optimization technique was created in the early 1960s and developed further in the 1970s and later by Ingo Rechenberg, Hans-Paul Schwefel and their co-workers. Methods Evolution strategies use natural problem-dependent representations, and primarily mutation and selection, as search operators. In common with evolutionary algorithms, the operators are applied in a loop. An iteration of the loop is called a generation. The sequence of generations is continued until a termination criterion is met. For real-valued search spaces, mutation is performed by adding a normally distributed random vector. The step size or mutation strength (i.e. the standard deviation of the normal distribution) is often governed by self-adaptation (see evolution window). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Drift
Genetic drift, also known as allelic drift or the Wright effect, is the change in the frequency of an existing gene variant (allele) in a population due to random chance. Genetic drift may cause gene variants to disappear completely and thereby reduce genetic variation. It can also cause initially rare alleles to become much more frequent and even fixed. When few copies of an allele exist, the effect of genetic drift is more notable, and when many copies exist, the effect is less notable. In the middle of the 20th century, vigorous debates occurred over the relative importance of natural selection versus neutral processes, including genetic drift. Ronald Fisher, who explained natural selection using Mendelian genetics, held the view that genetic drift plays at most a minor role in evolution, and this remained the dominant view for several decades. In 1968, population geneticist Motoo Kimura rekindled the debate with his neutral theory of molecular evolution, which claims that ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc. Methodology Optimization problems In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Human-based Computation
Human-based computation (HBC), human-assisted computation, ubiquitous human computing or distributed thinking (by analogy to distributed computing) is a computer science technique in which a machine performs its function by outsourcing certain steps to humans, usually as microwork. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human–computer interaction. For computationally difficult tasks such as image recognition, human-based computation plays a central role in training Deep Learning-based Artificial Intelligence systems. In this case, human-based computation has been referred to as human-aided artificial intelligence. In traditional computation, a human employs a computer to solve a problem; a human provides a formalized problem description and an algorithm to a computer, and receives a solution to interpret. Human-based computation frequently reverses the roles; the computer asks a person or a large ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
