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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
Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics
TL;DR: The proposed algorithm is to enhance the performance of GAs by introducing a greedy reformation scheme so as to have locally optimal offspring and has the best performance when compared to other existing search algorithms.
Journal ArticleDOI
Using genetic algorithms in sub-pixel mapping
TL;DR: Sub-pixel mapping is a technique designed to use the information contained in these mixed pixels to obtain a sharpened image using genetic algorithms combined with the assumption of spatial dependence to assign a location to every sub-pixel.
Book ChapterDOI
Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization
Xin-She Yang,Suash Deb +1 more
TL;DR: In this article, a two-stage hybrid search method, called Eagle Strategy, was proposed for stochastic optimization problems, which combines the random search using Levy walk with the firefly algorithm in an iterative manner.
Journal Article
Hybrid Genetic Algorithms: A Review
TL;DR: Different forms of integration between genetic algorithms and other search and optimization techniques are reviewed and several issues that need to be taken into consideration when designing a hybrid genetic algorithm that uses another search method as a local search tool are examined.
Journal ArticleDOI
ECG beat classification by a novel hybrid neural network.
Zümray Dokur,Tamer Ölmez +1 more
TL;DR: Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 96% by using the hybrid structure.
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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