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Everything about Stochastic universal sampling
Data sampling technique used in genetic algorithm
Stochastic universal sampling (SUS) is a selection technique used in evolutionary algorithms for selecting potentially useful solutions for recombination. It was introduced by James Baker. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Stochastic universal samplingA 5-minute orientation
- Pick up a classic book: Neural Information ProcessingThe most-reprinted book on the subject
- Read the landmark paper: Optimizing Feature Selection in Intrusion Detection Systems Using a Genetic Algorithm with Stochastic Universal SamplingCited 0 times
- Go sideways: Fitness proportionate selectionA closely related topic
- Go sideways: Selection (evolutionary algorithm)A closely related topic
đź“– Definitions
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🗂️ The subject at a glance
🏆 Landmark papers (most cited)
đź“š Books worth reading
Neural Information ProcessingTingwen Huang · 2012
Trends in stochastic analysisJochen Blath, Peter Mörters · 2009
Probability and ComputingMichael Mitzenmacher, Eli Upfal · 2005
Finitary probabilistic methods in econophysicsUbaldo Garibaldi · 2010
From finite sample to asymptotic methods in statisticsPranab Kumar Sen · 2009
Markov processes, Gaussian processes, and local timesMichael B. Marcus · 2006
Using a stochastic model in stratified samplingDonald M. Roberts · 1980- 📕Finite-sample properties of stochastic predictors in nonlinear systemsRoberto S. Mariano · 1985
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
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