Genetic algorithm The 2006 NASA ST5 spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields. Methodology[edit] In a genetic algorithm, a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions. A typical genetic algorithm requires: a genetic representation of the solution domain,a fitness function to evaluate the solution domain. Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and then to improve it through repetitive application of the mutation, crossover, inversion and selection operators. Initialization of genetic algorithm[edit] Selection[edit] Genetic operators[edit]
Evolutionary multimodal optimization In applied mathematics, multimodal optimization deals with optimization tasks that involve finding all or most of the multiple (at least locally optimal) solutions of a problem, as opposed to a single best solution. Evolutionary multimodal optimization is a branch of evolutionary computation, which is closely related to machine learning. Wong provides a short survey,[1] wherein the chapter of Shir[2] and the book of Preuss[3] cover the topic in more detail. Motivation[edit] Knowledge of multiple solutions to an optimization task is especially helpful in engineering, when due to physical (and/or cost) constraints, the best results may not always be realizable. Background[edit] Classical techniques of optimization would need multiple restart points and multiple runs in the hope that a different solution may be discovered every run, with no guarantee however. Multimodal optimization using genetic algorithms/evolution strategies[edit] References[edit] Bibliography[edit] D. External links[edit]
Human-based computation Human-based computation (HBC) is a computer science technique in which a machine performs its function by outsourcing certain steps to humans. This approach uses 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[1] 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 group of people to solve a problem, then collects, interprets, and integrates their solutions. Early work[edit] Human-based computation (apart from the historical meaning of "computer") research has its origins in the early work on interactive evolutionary computation. A concept of the automatic Turing test pioneered by Moni Naor (1996) is another precursor of human-based computation. Alternative terms[edit]
Ethical regulator An ethical regulator and a regulated system Ethical Regulator Theorem[edit] Mick Ashby's ethical regulator theorem[1] builds upon the Conant-Ashby good regulator theorem,[2] which is ambiguous because being good at regulating does not imply being good ethically. Of these requisites, only the first six are necessary for a regulator to be effective. Effectiveness of a regulator The ethical regulator theorem shows that the effectiveness of a cybernetic regulator depends on seven requisites. EffectivenessR = PurposeR x TruthR x (VarietyR - EthicsR) x PredictabilityR x IntelligenceR x InfluenceR If two systems, A and B, are competing for control of a third system, C, and EffectivenessA "is greater than EffectivenessB, then A is more likely than B to win control of C". Model-Centric Cybernetics Paradigm[edit] A cybernetic regulator consists of a purpose, model, well-defined observer, decision-making intelligence, and a control channel. See also[edit] References[edit]
Artificial neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] There is no single formal definition of what an artificial neural network is. consist of sets of adaptive weights, i.e. numerical parameters that are tuned by a learning algorithm, andare capable of approximating non-linear functions of their inputs. History[edit] Farley and Wesley A. Recent improvements[edit] Models[edit] and .
Eyeborg The Harbisson's Sonochromatic Music Scale An eyeborg or eye-borg is a body modification apparatus which fits on the wearer's head, and is designed to allow people to perceive color through sound waves. It works with a head-mounted antenna that senses the colors directly in front of a person, and converts them in real-time into sound waves through bone conduction.[1] History[edit] Color to sound scales[edit] Harbisson's Sonochromatic Music Scale (2003) is a microtonal and logarithmic scale with 360 notes in an octave. Harbisson's Pure Sonochromatic Scale (2005) is a non-logarithmic scale based on the transposition of light frequencies to sound frequencies. The blind[edit] Blind Ecuadorians using eyeborgs Eyeborgs are currently being treated as body parts rather than as devices, and therefore are donated rather than sold.[14] See also[edit] References[edit] External links[edit]
Engineering cybernetics Engineering cybernetics also known as technical cybernetics or cybernetic engineering, is the branch of cybernetics concerned with applications in engineering, in fields such as control engineering and robotics. History[edit] Qian Xuesen (Hsue-Shen Tsien) defined engineering cybernetics as a theoretical field of "engineering science",[1] the purpose of which is to "study those parts of the broad science of cybernetics which have direct engineering applications in designing controlled or guided systems".[2] Published in 1954, Qian's published work "Engineering Cybernetics" describes the mathematical and engineering concepts of cybernetic ideas as understood at the time, breaking them down into granular scientific concepts for application. Qian's work is notable for going beyond model-based theories and arguing for the necessity of a new design principle for types of system the properties and characteristics of which are largely unknown.[3] Popular usage[edit] In Media[edit] See also[edit]
Merrelyn Emery Australian social scientist She regards humans as "innately social animals; we grow only according to the density of interconnections we share with a group," and that "the basic unit of society is the group, not the individual".[6] These groups, be it families, communities, and organizations, is in OST seen as an open social system that transacts with its environment, the external social field, and co-evolution and active adaptation is needed for sustainability and harmony. Socioecology captures the notion of people-in-environments. Included within this is the concept of open, jointly optimized, sociotechnical systems, optimizing human purposefulness and creativity, and the best options afforded by changing technologies. Publications[edit] Merrelyn Emery is the author or coauthor of ten books, eight edited books, 35 book chapters, 60 journal articles, and contributed 29 institutional research reports including several national studies (e.g. Papers and articles, a selection: 1986.
Family therapy Field of psychology Family therapy (also referred to as family counseling, family systems therapy, marriage and family therapy, couple and family therapy) is a branch of psychology and clinical social work that works with families and couples in intimate relationships to nurture change and development. It tends to view change in terms of the systems of interaction between family members. The different schools of family therapy have in common a belief that, regardless of the origin of the problem, and regardless of whether the clients consider it an "individual" or "family" issue, involving families in solutions often benefits clients. This involvement of families is commonly accomplished by their direct participation in the therapy session. The skills of the family therapist thus include the ability to influence conversations in a way that catalyses the strengths, wisdom, and support of the wider system.[1] History and theoretical frameworks[edit] Techniques[edit] Evidence base[edit] [edit]
Elmer and Elsie (robots) Robotic vacuum cleaner Eigenform In mathematics, an eigenform (meaning simultaneous Hecke eigenform with modular group SL(2,Z)) is a modular form which is an eigenvector for all Hecke operators Tm, m = 1, 2, 3, .... Eigenforms fall into the realm of number theory, but can be found in other areas of math and science such as analysis, combinatorics, and physics. A common example of an eigenform, and the only non-cuspidal eigenforms, are the Eisenstein series. Another example is the Δ Function. In second-order cybernetics, eigenforms are an example of a self-referential system.[1] Normalization[edit] There are two different normalizations for an eigenform (or for a modular form in general). Algebraic normalization[edit] An eigenform is said to be normalized when scaled so that the q-coefficient in its Fourier series is one: where q = e2πiz. Analytic normalization[edit] An eigenform which is cuspidal can be normalized with respect to its inner product: Existence[edit] Higher levels[edit] In cybernetics[edit] References[edit]