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Genetic Algorithms
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The article was published on 2002-01-01 and is currently open access. It has received 17039 citations till now.read more
Citations
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Journal ArticleDOI
Image thresholding based on the EM algorithm and the generalized Gaussian distribution
TL;DR: A novel parametric and global image histogram thresholding method based on the estimation of the statistical parameters of ''object'' and ''background'' classes by the expectation-maximization (EM) algorithm, under the assumption that these two classes follow a generalized Gaussian (GG) distribution.
Journal ArticleDOI
Finding optimal control strategies for invasive species: a density‐structured model for Spartina alterniflora
Caz M. Taylor,Alan Hastings +1 more
TL;DR: Given the uncertainty in future budgets allocated for control of invasive Spartina, the basic question of whether it is more efficient to prioritize the removal of outliers or core populations of an invasive grass, Spartina alterniflora, is addressed.
Journal ArticleDOI
Secure multidimensional range queries over outsourced data
TL;DR: A tunable data bucketization algorithm is provided that allows the data owner to control the trade-off between disclosure risk and cost and is derived from cost and disclosure-risk metrics that estimate client’s computational overhead and disclosure risk respectively.
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On-road vehicle detection using evolutionary Gabor filter optimization
TL;DR: The objective is to build a set of filters that are capable of responding stronger to features present in vehicles than to nonvehicles, therefore improving class discrimination and unifies filter design with filter selection by integrating genetic algorithms (GAs) with an incremental clustering approach.
Journal ArticleDOI
Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm
Wenbing Tao,Jinwen Tian,Jian Liu +2 more
TL;DR: A three-level thresholding method for image segmentation is presented, based on probability partition, fuzzy partition and entropy theory, and a new fuzzy entropy has been defined through probability analysis.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
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An Introduction to Genetic Algorithms.
TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.
Book
Handbook of Genetic Algorithms
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
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An Introduction to Population Genetics Theory
James F. Crow,Motoo Kimura +1 more
TL;DR: An introduction to population genetics theory, An introduction to Population Genetics Theory, Population Genetics theory, Population genetics theory as discussed by the authors, Population genetics, population genetics, and population genetics theories, Population Genetic Theory