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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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On the convergence of a class of estimation of distribution algorithms
Qingfu Zhang,Heinz Mühlenbein +1 more
TL;DR: If the distribution of the new elements matches that of the parent set exactly, the algorithms will converge to the global optimum under three widely used selection schemes and a factorized distribution algorithm converges globally under proportional selection.
Journal ArticleDOI
A survey of techniques for characterising fitness landscapes and some possible ways forward
TL;DR: An overview of techniques from the 1980s to the present is provided, revealing the wide range of factors that can influence problem difficulty and emphasising the need for a shift in focus away from predicting problem hardness towards measuring characteristics.
Journal ArticleDOI
Visual sign information extraction and identification by deformable models for intelligent vehicles
TL;DR: A deformable model scheme allows us to include the knowledge used while designing the signs in the algorithm and is used for their detection and identification, and two techniques to find the minimum in the energy function are shown: simulated annealing and genetic algorithms.
Journal ArticleDOI
Multilevel minimum cross entropy threshold selection based on particle swarm optimization
TL;DR: A recursive programming technique is presented which reduces an order of magnitude for computing the minimum cross entropy thresholding objective function and a particle swarm optimization (PSO) algorithm is proposed for searching the near-optimal MCET thresholds.
Journal ArticleDOI
A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean
Jui-Sheng Chou,Dinh-Nhat Truong +1 more
TL;DR: The JS algorithm was used to solve structural optimization problems, including 25- bar tower design and 582-bar tower design problems, where JS not only performed best but also required the fewest evaluations of objective functions.
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