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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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Exploration and exploitation in evolutionary algorithms: A survey
TL;DR: A fresh treatment is introduced that classifies and discusses existing work within three rational aspects: what and how EA components contribute to exploration and exploitation; when and how Exploration and exploitation are controlled; and how balance between exploration and exploited is achieved.
Journal ArticleDOI
Survey of maneuvering target tracking. Part V. Multiple-model methods
X. Rong Li,Vesselin P. Jilkov +1 more
TL;DR: A comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty is presented in this article, which is centered around three generations of algorithms: autonomous, cooperating, and variable structure.
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A review on simulation-based optimization methods applied to building performance analysis
TL;DR: The review indicates that future researches should be oriented towards improving the efficiency of search techniques and approximation methods for large-scale building optimization problems; and reducing time and effort for such activities.
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Aquila Optimizer: A novel meta-heuristic optimization algorithm
Laith Abualigah,Dalia Yousri,Mohamed Abd Elaziz,Ahmed A. Ewees,Mohammed A. A. Al-qaness,Amir H. Gandomi +5 more
TL;DR: From the experimental results of AO that compared with well-known meta-heuristic methods, the superiority of the developed AO algorithm is observed.
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A comprehensive review of firefly algorithms
TL;DR: The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice as mentioned in this paper, and many problems from various areas have been successfully solved using the Firefly algorithm and its variants.
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