Stochastic universal sampling (SUS) is a selection technique used in evolutionary algorithms for selecting potentially useful solutions for recombination...

spaced pointers on a wheel that is spun once, it is called stochastic universal sampling. Repeatedly selecting the best individual of a randomly chosen...

techniques, such as stochastic universal sampling or tournament selection, are often used in practice. This is because they have less stochastic noise, or are...

Software Update Services, a software updating tool from Microsoft Stochastic universal sampling System usability scale, in systems engineering Club SuS 1896...

as a parent by the random spin of the wheel. Alternatively, stochastic universal sampling can be implemented. This selection method is also based on the...

set. It is common to refer to a sample space by the labels S, Ω, or U (for "universal set"). The elements of a sample space may be numbers, words, letters...

proportionate selection Selection (evolutionary algorithm) Stochastic universal sampling Tournament selection Loshchilov, I.; M. Schoenauer; M. Sebag...

sampling or Gibbs sampling. (However, Gibbs sampling, which breaks down a multi-dimensional sampling problem into a series of low-dimensional samples...

Stochastic block model Stochastic cellular automaton Stochastic diffusion search Stochastic grammar Stochastic matrix Stochastic universal sampling Stress...

proportionate selection – also known as roulette-wheel selection Stochastic universal sampling Truncation selection Tournament selection Memetic algorithm...

Inverse transform sampling (also known as inversion sampling, the inverse probability integral transform, the inverse transformation method, or the Smirnov...

desired results using this sampling method. Another method is random circuit sampling, in which the main task is to sample the output of a random quantum...

universal tool to data compression, but recently have been used to model data in different areas such as biology, linguistics and music. A stochastic...

In stochastic analysis, a rough path is a generalization of the notion of smooth path allowing to construct a robust solution theory for controlled differential...

(2012). "Controlling Variability in Split-Merge Systems". Analytical and Stochastic Modeling Techniques and Applications (PDF). Lecture Notes in Computer...

compromise is to use "mini-batches", small batches with samples in each batch selected stochastically from the entire data set. ANNs have evolved into a broad...

basis vector.: 15–17  This is known as the Born rule and appears as a stochastic non-reversible operation as it probabilistically sets the quantum state...

enhance resolution. Such methods include STED, GSD, RESOLFT and SSIM. Stochastic super-resolution: the chemical complexity of many molecular light sources...

the sampling design is correlated with the outcome of interest. For example, a possible sampling design might be such that each element in the sample may...

is the cardinality of X {\displaystyle {\mathcal {X}}} . For a general stochastic process {X(t)} with AEP, the (weakly) typical set can be defined similarly...

POS-tagging algorithms fall into two distinctive groups: rule-based and stochastic. E. Brill's tagger, one of the first and most widely used English POS-taggers...

became a standard reference in this area. By applying a lifting to a stochastic process, the Ionescu Tulceas obtained a ‘separable’ process; this gives...

bup are the lower and upper boundaries, respectively. Set the initial sampling range to cover the entire search-space (or a part of it): d = bup − blo...

Steganography Stochastic calculus Stochastic calculus of variations Stochastic geometry the study of random patterns of points Stochastic process Stratified...

this precise, a universal computer (or universal Turing machine) must be specified, so that "program" means a program for this universal machine. A random...

{s} )+\varepsilon ''} which he termed universal model of spatial variation. Both deterministic and stochastic components of spatial variation can be...

where X 1 , X 2 , … {\displaystyle X_{1},X_{2},\ldots } constitute a stochastic process and let θ ˘ ∣ X i {\displaystyle {\breve {\theta }}\mid X^{i}}...

Dynamic stochastic general equilibrium modeling (abbreviated as DSGE, or DGE, or sometimes SDGE) is a macroeconomic method which is often employed by monetary...

random sampling of any variable, rather than to the mean values (or sums) of iid random variables extracted from a population by repeated sampling. That...

Bernhard; Maass, Wolfgang (3 November 2011). "Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons". PLOS...