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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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A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation
TL;DR: Experiments and comparative results with multilevel thresholding methods over a synthetic histogram and real images show the efficiency of the proposed method.
Journal ArticleDOI
How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration
TL;DR: This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools' relative effectiveness in calibrating hydrologic models and e-NSGAII appears to be superior to MOSCEM-UA and competitive with SPEA2 for Hydrologic model calibration.
Journal ArticleDOI
Recent advances in engineering design optimisation: Challenges and future trends
TL;DR: This paper identifies recent approaches to automating the manual optimisation process and the challenges that it presents to the engineering community.
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Broadband Electromagnetic Absorbers Using Carbon Nanostructure-Based Composites
Davide Micheli,Roberto Pastore,Carmelo Apollo,Mario Marchetti,Gabriele Gradoni,Valter Mariani Primiani,Franco Moglie +6 more
TL;DR: In this article, the authors present the design of nanostructured multilayer absorbers, carried out with the aid of a genetic algorithm (GA), where conductive fillers are uniformly dispersed in an epoxy resin at different weight percentages (1, 3, 5 wt.).
Journal ArticleDOI
The Diversity-Bandwidth Tradeoff
TL;DR: Social network and e-mail content from an executive recruiting firm show that bridging ties can actually offer less novelty for these reasons, suggesting that the strength of weak ties and structural holes depend on brokers’ information environments.
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.
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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