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Interactive Evolutionary Computation
Interactive evolutionary computation (IEC) or aesthetic selection is a general term for methods of evolutionary computation that use human evaluation. Usually human evaluation is necessary when the form of fitness function is not known (for example, visual appeal or attractiveness; as in Dawkins, 1986) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface). IEC design issues The number of evaluations that IEC can receive from one human user is limited by user fatigue which was reported by many researchers as a major problem. In addition, human evaluations are slow and expensive as compared to fitness function computation. Hence, one-user IEC methods should be designed to converge using a small number of evaluations, which necessarily implies very small populations. Several methods were proposed by researchers to speed up convergence, like interactive constrain evolutionary search (user intervention) or ... [...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] |
Genetic Algorithms
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] |
User Review
A user review is a review conducted by any person who has access to the internet and publishes their experience to a review site or social media platform following product testing or the evaluation of a service. User reviews are commonly provided by consumers who volunteer to write the review, rather than professionals who are paid to evaluate the product or service. User reviews might be compared to professional nonprofit reviews from a consumer organization, or to promotional reviews from an advertiser or company marketing a product. Growth of social media platforms has enabled the facilitation of interaction between consumers after a review has been placed on online communities such as blogs, internet forums or other popular platforms. Purpose of user reviews User reviews guide stakeholders, including consumers, producers, and competitors decision making process regarding the good or service experienced by the user providing the review. Purchase decisions can be made with ea ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Electric Sheep
Electric Sheep is a volunteer computing project for animating and evolving fractal flames, which are in turn distributed to the networked computers, which display them as a screensaver. Process The process is transparent to the casual user, who can simply install the software as a screensaver. Alternatively, the user may become more involved with the project, manually creating a fractal flame file for upload to the server where it is rendered into a video file of the animated fractal flame. As the screensaver entertains the user, their computer is also used for rendering commercial projects, sales of which keep the servers and developers running. There are about 500,000 active users (monthly uniques). According to Mitchell Whitelaw in his ''Metacreation: Art and Artificial Life'', "On the screen they are luminous, twisting, elastic shapes, abstract tangles and loops of glowing filaments." The name "Electric Sheep" is taken from the title of Philip K. Dick's novel ''Do And ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Human–computer Interaction
Human–computer interaction (HCI) is research in the design and the use of computer technology, which focuses on the interfaces between people (users) and computers. HCI researchers observe the ways humans interact with computers and design technologies that allow humans to interact with computers in novel ways. A device that allows interaction between human being and a computer is known as a "Human-computer Interface (HCI)". As a field of research, human–computer interaction is situated at the intersection of computer science, behavioral sciences, design, media studies, and several other fields of study. The term was popularized by Stuart K. Card, Allen Newell, and Thomas P. Moran in their 1983 book, ''The Psychology of Human–Computer Interaction.'' The first known use was in 1975 by Carlisle. The term is intended to convey that, unlike other tools with specific and limited uses, computers have many uses which often involve an open-ended dialogue between the user and t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Human-based Genetic Algorithm
In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical genetic algorithm to humans. Evolutionary genetic systems and human agency Among evolutionary genetic systems, HBGA is the computer-based analogue of genetic engineering (Allan, 2005). This table compares systems on lines of human agency: One obvious pattern in the table is the division between organic (top) and computer systems (bottom). Another is the vertical symmetry between autonomous systems (top and bottom) and human-interactive systems (middle). Looking to the right, the ''selector'' is the agent that decides fitness in the system. It determines which variations will reproduce and con ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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 Art
Evolutionary art is a branch of generative art, in which the artist does not do the work of constructing the artwork, but rather lets a system do the construction. In evolutionary art, initially generated art is put through an iterated process of selection and modification to arrive at a final product, where it is the artist who is the selective agent. Evolutionary art is to be distinguished from BioArt, which uses living organisms as the material medium instead of paint, stone, metal, etc. Overview In common with biological evolution through natural selection or animal husbandry, the members of a population undergoing artificial evolution modify their form or behavior over many reproductive generations in response to a selective regime. In interactive evolution the selective regime may be applied by the viewer explicitly by selecting individuals which are aesthetically pleasing. Alternatively a selection pressure can be generated implicitly, for example according to the leng ... [...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] |
Fitness Function
{{no footnotes, date=May 2015 A fitness function is a particular type of objective function that is used to summarise, as a single figure of merit, how close a given design solution is to achieving the set aims. Fitness functions are used in genetic programming and genetic algorithms to guide simulations towards optimal design solutions. Genetic programming and algorithms In particular, in the fields of genetic programming and genetic algorithms, each design solution is commonly represented as a string of numbers (referred to as a chromosome). After each round of testing, or simulation, the idea is to delete the ''n'' worst design solutions, and to breed ''n'' new ones from the best design solutions. Each design solution, therefore, needs to be awarded a figure of merit, to indicate how close it came to meeting the overall specification, and this is generated by applying the fitness function to the test, or simulation, results obtained from that solution. The reason that genetic al ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Human-based Genetic Algorithm
In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical genetic algorithm to humans. Evolutionary genetic systems and human agency Among evolutionary genetic systems, HBGA is the computer-based analogue of genetic engineering (Allan, 2005). This table compares systems on lines of human agency: One obvious pattern in the table is the division between organic (top) and computer systems (bottom). Another is the vertical symmetry between autonomous systems (top and bottom) and human-interactive systems (middle). Looking to the right, the ''selector'' is the agent that decides fitness in the system. It determines which variations will reproduce and con ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
