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A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. Graphical models are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine... | Wikipedia/Graphical_models |
In electrical engineering, statistical computing and bioinformatics, the Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step.... | Wikipedia/Baum–Welch_algorithm |
In computer science, the precision of a numerical quantity is a measure of the detail in which the quantity is expressed. This is usually measured in bits, but sometimes in decimal digits. It is related to precision in mathematics, which describes the number of digits that are used to express a value.
Some of the stand... | Wikipedia/Precision_(computer_science) |
Deep Learning Super Sampling (DLSS) is a suite of real-time deep learning image enhancement and upscaling technologies developed by Nvidia that are available in a number of video games. The goal of these technologies is to allow the majority of the graphics pipeline to run at a lower resolution for increased performanc... | Wikipedia/Tensor_Core |
Thermal design power (TDP), also known as thermal design point, is the maximum amount of heat that a computer component (like a CPU, GPU or system on a chip) can generate and that its cooling system is designed to dissipate during normal operation at a non-turbo clock rate (base frequency).
Some sources state that the ... | Wikipedia/Thermal_design_power |
Mutation is a genetic operator used to maintain genetic diversity of the chromosomes of a population of an evolutionary algorithm (EA), including genetic algorithms in particular. It is analogous to biological mutation.
The classic example of a mutation operator of a binary coded genetic algorithm (GA) involves a proba... | Wikipedia/Mutation_(evolutionary_algorithm) |
Ecological interface design (EID) is an approach to interface design that was introduced specifically for complex sociotechnical, real-time, and dynamic systems. It has been applied in a variety of domains including process control (e.g. nuclear power plants, petrochemical plants), aviation, and medicine.
EID differs f... | Wikipedia/Ecological_interface_design |
In numerical linear algebra, the conjugate gradient method is an iterative method for numerically solving the linear system
A
x
=
b
{\displaystyle {\boldsymbol {Ax}}={\boldsymbol {b}}}
where
... | Wikipedia/Derivation_of_the_conjugate_gradient_method |
In mathematics, more specifically in numerical linear algebra, the biconjugate gradient method is an algorithm to solve systems of linear equations
A
x
=
b
.
{\displaystyle Ax=b.\,}
Unlike the conjugate gradient method, this algorithm ... | Wikipedia/Biconjugate_gradient_method |
Feedback occurs when outputs of a system are routed back as inputs as part of a chain of cause and effect that forms a circuit or loop. The system can then be said to feed back into itself. The notion of cause-and-effect has to be handled carefully when applied to feedback systems:
Simple causal reasoning about a feed... | Wikipedia/Feedback_Control |
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differen... | Wikipedia/Normal_equations |
In numerical linear algebra, the conjugate gradient squared method (CGS) is an iterative algorithm for solving systems of linear equations of the form
A
x
=
b
{\display... | Wikipedia/Conjugate_gradient_squared_method |
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
The direct modeling of lik... | Wikipedia/Flow-based_generative_model |
A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. Convolution-based networks are th... | Wikipedia/Deconvolutional_neural_network |
In experimental particle physics, a calorimeter is a type of detector that measures the energy of particles. Particles enter the calorimeter and initiate a particle shower in which their energy is deposited in the calorimeter, collected, and measured. The energy may be measured in its entirety, requiring total contai... | Wikipedia/Calorimeter_(particle_physics) |
In mathematics, a Borel set is any subset of a topological space that can be formed from its open sets (or, equivalently, from closed sets) through the operations of countable union, countable intersection, and relative complement. Borel sets are named after Émile Borel.
For a topological space X, the collection of al... | Wikipedia/Borel_Algebra |
Wi-Fi calling, also called VoWiFi, refers to mobile phone voice calls and data that are made over IP networks using Wi-Fi, instead of the cell towers provided by cellular networks. Using this feature, compatible handsets are able to route regular cellular calls through a wireless LAN (Wi-Fi) network with broadband Inte... | Wikipedia/Generic_Access_Network |
In combinatorial mathematics and extremal set theory, the Sauer–Shelah lemma states that every family of sets with small VC dimension consists of a small number of sets. It is named after Norbert Sauer and Saharon Shelah, who published it independently of each other in 1972. The same result was also published slightly ... | Wikipedia/Sauer–Shelah_lemma |
Algorithmic accountability refers to the allocation of responsibility for the consequences of real-world actions influenced by algorithms used in decision-making processes.
Ideally, algorithms should be designed to eliminate bias from their decision-making outcomes. This means they ought to evaluate only relevant char... | Wikipedia/Algorithmic_accountability |
Codeforces (Russian: Коудфорсес) is a website that hosts competitive programming contests. It is maintained by a group of competitive programmers from ITMO University led by Mikhail Mirzayanov. Since 2013, Codeforces claims to surpass TopCoder in terms of active contestants. As of 2019, it has over 600,000 registered u... | Wikipedia/Codeforces |
Algorithms of Oppression: How Search Engines Reinforce Racism is a 2018 book by Safiya Umoja Noble in the fields of information science, machine learning, and human-computer interaction.
== Background ==
Noble earned an undergraduate degree in sociology from California State University, Fresno in the 1990s, then work... | Wikipedia/Algorithms_of_Oppression |
Science, technology, engineering, and mathematics (STEM) is an umbrella term used to group together the distinct but related technical disciplines of science, technology, engineering, and mathematics. The term is typically used in the context of education policy or curriculum choices in schools. It has implications for... | Wikipedia/Science,_technology,_engineering,_and_mathematics |
A disease of despair is one of three classes of behavior-related medical conditions that increase in groups of people who experience despair due to a sense that their long-term social and economic prospects are bleak. The three disease types are drug overdose, including alcohol overdose, suicide, and alcoholic liver di... | Wikipedia/Diseases_of_despair |
Geographical segregation exists whenever the proportions of population rates of two or more populations are not homogeneous throughout a defined space. Populations can be considered any plant or animal species, human genders, followers of a certain religion, people of different nationalities, ethnic groups, etc.
In soc... | Wikipedia/Geographical_segregation |
Diagnoses of autism have become more frequent since the 1980s, which has led to various controversies about both the cause of autism and the nature of the diagnoses themselves. Whether autism has mainly a genetic or developmental cause, and the degree of coincidence between autism and intellectual disability, are all m... | Wikipedia/Medical_model_of_autism |
Disparate impact in the law of the United States refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute... | Wikipedia/Disparate_impact |
Discrimination against people with substance use disorders is a form of discrimination against people with this disease. In the United States, people with substance use disorders are often blamed for their disease, which is often seen as a moral failing, due to a lack of public understanding about substance use disorde... | Wikipedia/Discrimination_against_drug_addicts |
The breadwinner model is a paradigm of family centered on a breadwinner, "the member of a family who earns the money to support the others." Traditionally, the earner works outside the home to provide the family with income and benefits such as health insurance, while the non-earner stays at home and takes care of chil... | Wikipedia/Breadwinner_model |
In law, selective enforcement occurs when government officials (such as police officers, prosecutors, or regulators) exercise discretion, which is the power to choose whether or how to punish a person who has violated the law. The biased use of enforcement discretion, such as that based on racial prejudice or corruptio... | Wikipedia/Selective_enforcement |
In sociology, societal transformation refers to “a deep and sustained, nonlinear systemic change” in a society. Transformational changes can occur within a particular system, such as a city, a transport or energy system. Societal transformations can also refer to changes of an entire culture or civilization. Such tran... | Wikipedia/Societal_transformation |
A latent variable model is a statistical model that relates a set of observable variables (also called manifest variables or indicators) to a set of latent variables. Latent variable models are applied across a wide range of fields such as biology, computer science, and social science. Common use cases for latent varia... | Wikipedia/Latent_variable_model |
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep ... | Wikipedia/Machine_learning_model |
A semantic similarity network (SSN) is a special form of semantic network. designed to represent concepts and their semantic similarity. Its main contribution is reducing the complexity of calculating semantic distances. Bendeck (2004, 2008) introduced the concept of semantic similarity networks (SSN) as the specializa... | Wikipedia/Semantic_similarity_network |
The bag-of-words (BoW) model is a model of text which uses an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity.
The bag-of-words model is commonly used in methods of... | Wikipedia/Bag-of-words_model |
The factored language model (FLM) is an extension of a conventional language model introduced by Jeff Bilmes and Katrin Kirchoff in 2003. In an FLM, each word is viewed as a vector of k factors:
w
i
=
{
f
... | Wikipedia/Factored_language_model |
Katz back-off is a generative n-gram language model that estimates the conditional probability of a word given its history in the n-gram. It accomplishes this estimation by backing off through progressively shorter history models under certain conditions. By doing so, the model with the most reliable information about ... | Wikipedia/Katz's_back-off_model |
Lemmatization (or less commonly lemmatisation) in linguistics is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form.
In computational linguistics, lemmatization is the algorithmic process of determining the lemma of... | Wikipedia/Lemmatisation |
A word n-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous w... | Wikipedia/Word_n-gram_language_model |
The Conference and Workshop on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held every December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial int... | Wikipedia/Advances_in_Neural_Information_Processing_Systems |
The transformer is a deep learning architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. At each layer, each token is then contextualized within the scope of the c... | Wikipedia/Transformer_(machine_learning) |
GraphPad Software Inc. was a privately held software development corporation until its acquisition by Insight Partners in 2017. The company was named Insightful Science, which itself merged with Dotmatics in 2021. The original software was written by Harvey Motulsky in 1989 and it was co-founded by Motulsky and Earl ... | Wikipedia/GraphPad_InStat |
GraphPad Software Inc. was a privately held software development corporation until its acquisition by Insight Partners in 2017. The company was named Insightful Science, which itself merged with Dotmatics in 2021. The original software was written by Harvey Motulsky in 1989 and it was co-founded by Motulsky and Earl ... | Wikipedia/GraphPad_Prism |
Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series, where the order of elements is important. Unlike feedforward neural networks, which process inputs independently, RNNs utilize recurrent connections, where the outpu... | Wikipedia/Recurrent_neural_networks |
Spacecraft attitude control is the process of controlling the orientation of a spacecraft (vehicle or satellite) with respect to an inertial frame of reference or another entity such as the celestial sphere, certain fields, and nearby objects, etc.
Controlling vehicle attitude requires actuators to apply the torques ne... | Wikipedia/Spacecraft_attitude_control |
In statistical hypothesis testing, a type I error, or a false positive, is the erroneous rejection of a true null hypothesis. A type II error, or a false negative, is the erroneous failure in bringing about appropriate rejection of a false null hypothesis.
Type I errors can be thought of as errors of commission, in whi... | Wikipedia/Type_I_error_rate |
In statistics, family-wise error rate (FWER) is the probability of making one or more false discoveries, or type I errors when performing multiple hypotheses tests.
== Familywise and experimentwise error rates ==
John Tukey developed in 1953 the concept of a familywise error rate as the probability of making a Type I... | Wikipedia/Family-wise_error_rate |
In statistics, a false coverage rate (FCR) is the average rate of false coverage, i.e. not covering the true parameters, among the selected intervals.
The FCR gives a simultaneous coverage at a (1 − α)×100% level for all of the parameters considered in the problem. The FCR has a strong connection to the false discovery... | Wikipedia/False_coverage_rate |
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised le... | Wikipedia/Reinforcement_Learning |
Metalearning is a neuroscientific term proposed by Kenji Doya, as a theory for how neurotransmitters facilitate distributed learning mechanisms in the Basal Ganglia. The theory primarily involves the role of neurotransmitters in dynamically adjusting the way computational learning algorithms interact to produce the kin... | Wikipedia/Metalearning_(neuroscience) |
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep ... | Wikipedia/Learning_algorithms |
In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover.
Most often, it is used for classification, as a k-NN classifier, the output of which is a class membership. An obje... | Wikipedia/K-nearest_neighbor_algorithm |
A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on two different input vectors to compute comparable output vectors. Often one of the output vectors is precomputed, thus forming a baseline against which the other output... | Wikipedia/Siamese_neural_network |
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or signal pathways. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural networks.
In neuroscience... | Wikipedia/Neural_Network |
A cellular network or mobile network is a telecommunications network where the link to and from end nodes is wireless and the network is distributed over land areas called cells, each served by at least one fixed-location transceiver (such as a base station). These base stations provide the cell with the network covera... | Wikipedia/Cellular_networks |
Power control, broadly speaking, is the intelligent selection of transmitter power output in a communication system to achieve good performance within the system. The notion of "good performance" can depend on context and may include optimizing metrics such as link data rate, network capacity, outage probability, geog... | Wikipedia/Power_control |
Traffic management is a key branch within logistics. It concerns the planning, control and purchasing of transport services needed to physically move vehicles (for example aircraft, road vehicles, rolling stock and watercraft) and freight.
Traffic management is implemented by people working with different job titles in... | Wikipedia/Traffic_control |
Salesforce, Inc. is an American cloud-based software company headquartered in San Francisco, California. It provides applications focused on sales, customer service, marketing automation, e-commerce, analytics, artificial intelligence, and application development.
Founded by former Oracle executive Marc Benioff in Marc... | Wikipedia/Salesforce |
In mathematics, the inverse trigonometric functions (occasionally also called antitrigonometric, cyclometric, or arcus functions) are the inverse functions of the trigonometric functions, under suitably restricted domains. Specifically, they are the inverses of the sine, cosine, tangent, cotangent, secant, and cosecant... | Wikipedia/Arctangent_function |
In statistics, a power transform is a family of functions applied to create a monotonic transformation of data using power functions. It is a data transformation technique used to stabilize variance, make the data more normal distribution-like, improve the validity of measures of association (such as the Pearson correl... | Wikipedia/Box–Cox_transformation |
RMI, or Rocky Mountain Institute, is a global, independent, non-partisan non-profit organization co-founded in the United States by Amory Lovins. As of 2025, RMI's stated mission is to transform "global energy systems through market-driven solutions to secure a prosperous, resilient, clean energy future for all." Estab... | Wikipedia/RMI_(energy_organization) |
In biochemistry and pharmacology, the Hill equation refers to two closely related equations that reflect the binding of ligands to macromolecules, as a function of the ligand concentration. A ligand is "a substance that forms a complex with a biomolecule to serve a biological purpose", and a macromolecule is a very lar... | Wikipedia/Hill–Langmuir_equation |
IEEE Transactions on Neural Networks and Learning Systems is a monthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers the theory, design, and applications of neural networks and related learning systems. According to the Journal Citation Reports, the journal had a 2... | Wikipedia/IEEE_Transactions_on_Neural_Networks |
In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the non-negative part of its argument, i.e., the ramp function:
ReLU
(
x
)
=
x
... | Wikipedia/Softplus_function |
The generalized logistic function or curve is an extension of the logistic or sigmoid functions. Originally developed for growth modelling, it allows for more flexible S-shaped curves. The function is sometimes named Richards's curve after F. J. Richards, who proposed the general form for the family of models in 1959.
... | Wikipedia/Generalised_logistic_function |
A bell-shaped function or simply 'bell curve' is a mathematical function having a characteristic "bell"-shaped curve. These functions are typically continuous or smooth, asymptotically approach zero for large negative/positive x, and have a single, unimodal maximum at small x. Hence, the integral of a bell-shaped funct... | Wikipedia/Bell_shaped_function |
The Van Genuchten–Gupta model is an inverted S-curve applicable to crop yield and soil salinity relations. It is named after Martinus Theodore van Genuchten and Satyandra K. Gupta's work from the 1990s.
== Equation ==
The mathematical expression is:
Y
=
... | Wikipedia/Van_Genuchten–Gupta_model |
BioMed Central (BMC) is a United Kingdom-based, for-profit scientific open access publisher that produces over 250 scientific journals. All its journals are published online only. BioMed Central describes itself as the first and largest open access science publisher. It was founded in 2000 and has been owned by Springe... | Wikipedia/BMC_Medical_Research_Methodology |
Variable-order Bayesian network (VOBN) models provide an important extension of both the Bayesian network models and the variable-order Markov models. VOBN models are used in machine learning in general and have shown great potential in bioinformatics applications.
These models extend the widely used position weight ma... | Wikipedia/Variable-order_Bayesian_network |
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.
When trained on a set of examples without supe... | Wikipedia/Deep_belief_network |
In the mathematical field of graph theory, the intersection number of a graph
G
=
(
V
,
E
)
{\displaystyle G=(V,E)}
is the smallest number of elements in a representation of
G
{\displaysty... | Wikipedia/Intersection_number_(graph_theory) |
In decision theory, a scoring rule provides evaluation metrics for probabilistic predictions or forecasts. While "regular" loss functions (such as mean squared error) assign a goodness-of-fit score to a predicted value and an observed value, scoring rules assign such a score to a predicted probability distribution and ... | Wikipedia/Scoring_function |
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. Graphical models are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine... | Wikipedia/Probabilistic_graphical_model |
In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs (also known as path diagrams, causal Bayesian networks or DAGs) are probabilistic graphical models used to encode assumptions about the data-generating process.
Causal graphs can be used for communication and for inference. They ... | Wikipedia/Causal_graph |
Feedforward refers to recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights to obtain outputs (inputs-to-output): feedforward. Recurrent neural networks, or neural networks with loops allow information from later processing stages to fee... | Wikipedia/Feed-forward_network |
"A Logical Calculus of the Ideas Immanent to Nervous Activity" is a 1943 article written by Warren McCulloch and Walter Pitts. The paper, published in the journal The Bulletin of Mathematical Biophysics, proposed a mathematical model of the nervous system as a network of simple logical elements, later known as artifici... | Wikipedia/A_Logical_Calculus_of_the_Ideas_Immanent_in_Nervous_Activity |
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a ... | Wikipedia/0-1_loss_function |
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a ... | Wikipedia/Loss_functions |
A wireless ad hoc network (WANET) or mobile ad hoc network (MANET) is a decentralized type of wireless network. The network is ad hoc because it does not rely on a pre-existing infrastructure, such as routers or wireless access points. Instead, each node participates in routing by forwarding data for other nodes. The d... | Wikipedia/Mobile_ad_hoc_network |
fNetwork emulation is a technique for testing the performance of real applications over a virtual network. This is different from network simulation where virtual models of traffic, network models, channels, and protocols are applied. The aim is to assess performance, predict the impact of change, or otherwise optimize... | Wikipedia/Network_emulation |
A traffic generation model is a stochastic model of the traffic flows or data sources in a communication network, for example a cellular network or a computer network. A packet generation model is a traffic generation model of the packet flows or data sources in a packet-switched network. For example, a web traffic mod... | Wikipedia/Traffic_generation_model |
A cognitive radio (CR) is a radio that can be programmed and configured dynamically to use the best channels in its vicinity to avoid user interference and congestion. Such a radio automatically detects available channels, then accordingly changes its transmission or reception parameters to allow more concurrent wirele... | Wikipedia/Cognitive_Radio_Networks |
The noisy channel model is a framework used in spell checkers, question answering, speech recognition, and machine translation. In this model, the goal is to find the intended word given a word where the letters have been scrambled in some manner.
== In spell-checking ==
See Chapter B of.
Given an alphabet
... | Wikipedia/Noisy_channel_model |
Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They are typically used in complex statistical models consisting of observed variables (usually termed "data") as well as unknown parameters and latent variables, with var... | Wikipedia/Variational_inference |
Super-resolution imaging (SR) is a class of techniques that improve the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced.
In some radar and sonar imaging applications (e.g. magnetic resonance ima... | Wikipedia/Super-resolution |
In version control systems, a repository is a data structure that stores metadata for a set of files or directory structure. Depending on whether the version control system in use is distributed, like Git or Mercurial, or centralized, like Subversion, CVS, or Perforce, the whole set of information in the repository may... | Wikipedia/Repository_(version_control) |
Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calcul... | Wikipedia/Adam_(optimization_algorithm) |
The Open Neural Network Exchange (ONNX) [ˈɒnɪks] is an open-source artificial intelligence ecosystem of technology companies and research organizations that establish open standards for representing machine learning algorithms and software tools to promote innovation and collaboration in the AI sector. ONNX is availab... | Wikipedia/Open_Neural_Network_Exchange |
In information theory, the cross-entropy between two probability distributions
p
{\displaystyle p}
and
q
{\displaystyle q}
, over the same underlying set of events, measures the average number of bits needed to identify an event draw... | Wikipedia/Cross_entropy |
Differential evolution (DE) is an evolutionary algorithm to optimize a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such methods are commonly known as metaheuristics as they make few or no assumptions about the optimized problem and can search very large space... | Wikipedia/Differential_evolution |
In artificial immune systems, clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory... | Wikipedia/Clonal_selection_algorithm |
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learn... | Wikipedia/Population_Based_Bio-Inspired_Algorithms |
A cellular evolutionary algorithm (cEA) is a kind of evolutionary algorithm (EA) in which individuals cannot mate arbitrarily, but every one interacts with its closer neighbors on which a basic EA is applied (selection, variation, replacement).
The cellular model simulates natural evolution from the point of view of
t... | Wikipedia/Cellular_evolutionary_algorithm |
In mathematical optimization and computer science, a feasible region, feasible set, or solution space is the set of all possible points (sets of values of the choice variables) of an optimization problem that satisfy the problem's constraints, potentially including inequalities, equalities, and integer constraints. Thi... | Wikipedia/Candidate_solution |
The promoter based genetic algorithm (PBGA) is a genetic algorithm for neuroevolution developed by F. Bellas and R.J. Duro in the Integrated Group for Engineering Research (GII) at the University of Coruña, in Spain. It evolves variable size feedforward artificial neural networks (ANN) that are encoded into sequences o... | Wikipedia/Promoter_based_genetic_algorithm |
Natural evolution strategies (NES) are a family of numerical optimization algorithms for black box problems. Similar in spirit to evolution strategies, they iteratively update the (continuous) parameters of a search distribution by following the natural gradient towards higher expected fitness.
== Method ==
The gener... | Wikipedia/Natural_evolution_strategy |
Cultural algorithms (CA) are a branch of evolutionary computation where there is a knowledge component that is called the belief space in addition to the population component. In this sense, cultural algorithms can be seen as an extension to a conventional genetic algorithm. Cultural algorithms were introduced by Reyno... | Wikipedia/Cultural_algorithm |
In mathematical optimization, the Rosenbrock function is a non-convex function, introduced by Howard H. Rosenbrock in 1960, which is used as a performance test problem for optimization algorithms. It is also known as Rosenbrock's valley or Rosenbrock's banana function.
The global minimum is inside a long, narrow, parab... | Wikipedia/Rosenbrock_function |
Evolution strategy (ES) from computer science is a subclass of evolutionary algorithms, which serves as an optimization technique. It uses the major genetic operators mutation, recombination and selection of parents.
== History ==
The 'evolution strategy' optimization technique was created in the early 1960s and deve... | Wikipedia/Evolution_strategy |
In computer science and operations research, the bees algorithm is a population-based search algorithm which was developed by Pham, Ghanbarzadeh et al. in 2005. It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global se... | Wikipedia/Bees_algorithm |
A schema (pl.: schemata) is a template in computer science used in the field of genetic algorithms that identifies a subset of strings with similarities at certain string positions. Schemata are a special case of cylinder sets, forming a basis for a product topology on strings. In other words, schemata can be used to g... | Wikipedia/Schema_(genetic_algorithms) |
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