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- Stochastic_universal_sampling abstract "Stochastic universal sampling (SUS) is a technique used in genetic algorithms for selecting potentially useful solutions for recombination. It was introduced by James Baker.SUS is a development of fitness proportionate selection (FPS) which exhibits no bias and minimal spread. Where FPS chooses several solutions from the population by repeated random sampling, SUS uses a single random value to sample all of the solutions by choosing them at evenly spaced intervals. This gives weaker members of the population (according to their fitness) a chance to be chosen and thus reduces the unfair nature of fitness-proportional selection methods. Other methods like roulette wheel can have bad performance when a member of the population has a really large fitness in comparison with other members. Using a comb-like ruler, SUS starts from a small random number, and chooses the next candidates from the rest of population remaining, not allowing the fittest members to saturate the candidate space.Described as an algorithm, pseudocode for SUS looks like:SUS(Population, N) F := total fitness of population N := number of offspring to keep P := distance between the pointers (F/N) Start := random number between 0 and P Pointers := [Start + i*P | i in [0..N-1]] return RWS(Population,Pointers)RWS(Population, Points) Keep = [] i := 0 for P in Points while fitness of Population[i] < P i++ add Population[i] to Keep return KeepHere RWS describes the bulk of fitness proportionate selection (also known as "roulette wheel selection") - in true fitness proportional selection the parameter Points is always a (sorted) list of random numbers from 0 to F. The algorithm above is intended to be illustrative rather than canonical.".
- Stochastic_universal_sampling thumbnail Statistically_Uniform.png?width=300.
- Stochastic_universal_sampling wikiPageID "8885706".
- Stochastic_universal_sampling wikiPageRevisionID "556574798".
- Stochastic_universal_sampling hasPhotoCollection Stochastic_universal_sampling.
- Stochastic_universal_sampling subject Category:Genetic_algorithms.
- Stochastic_universal_sampling subject Category:Stochastic_algorithms.
- Stochastic_universal_sampling type Abstraction100002137.
- Stochastic_universal_sampling type Act100030358.
- Stochastic_universal_sampling type Activity100407535.
- Stochastic_universal_sampling type Algorithm105847438.
- Stochastic_universal_sampling type Event100029378.
- Stochastic_universal_sampling type GeneticAlgorithms.
- Stochastic_universal_sampling type Procedure101023820.
- Stochastic_universal_sampling type PsychologicalFeature100023100.
- Stochastic_universal_sampling type Rule105846932.
- Stochastic_universal_sampling type StochasticAlgorithms.
- Stochastic_universal_sampling type YagoPermanentlyLocatedEntity.
- Stochastic_universal_sampling comment "Stochastic universal sampling (SUS) is a technique used in genetic algorithms for selecting potentially useful solutions for recombination. It was introduced by James Baker.SUS is a development of fitness proportionate selection (FPS) which exhibits no bias and minimal spread. Where FPS chooses several solutions from the population by repeated random sampling, SUS uses a single random value to sample all of the solutions by choosing them at evenly spaced intervals.".
- Stochastic_universal_sampling label "Stochastic universal sampling".
- Stochastic_universal_sampling sameAs m.027n960.
- Stochastic_universal_sampling sameAs Q16335224.
- Stochastic_universal_sampling sameAs Q16335224.
- Stochastic_universal_sampling sameAs Stochastic_universal_sampling.
- Stochastic_universal_sampling wasDerivedFrom Stochastic_universal_sampling?oldid=556574798.
- Stochastic_universal_sampling depiction Statistically_Uniform.png.
- Stochastic_universal_sampling isPrimaryTopicOf Stochastic_universal_sampling.