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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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Optimizing the schedule of dispatching RMC trucks through genetic algorithms
TL;DR: In this article, the authors developed a systematic model that optimizes the schedule of dispatching RMC trucks, which balances the operations at the construction sites and the batch plant, to find the best dispatching schedule which minimizes the total waiting duration at construction sites, and satisfies the needs of RMC deliveries from different construction sites.
Journal ArticleDOI
Efficient Nondomination Level Update Method for Steady-State Evolutionary Multiobjective Optimization
TL;DR: This paper proposes an efficient NDL update method to reduce the cost for maintaining the NDL structure in steady-state EMO, and finds that the proposed method is also useful for the generational evolution model.
Journal ArticleDOI
A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection
TL;DR: The statistical analysis of the obtained results provides evidence to support the claim that cooperative P-EA is more efficient and effective than state of the art detection approaches based on a benchmark of nine large open source systems.
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Designing optimal multiple tuned mass dampers using genetic algorithms (GAs) for mitigating the seismic response of structures
TL;DR: In this paper, an effective method has been proposed for designing optimal multiple tuned mass dampers (MTMDs) to mitigate the seismic response of structures based on defining an optimization problem which considers the parameters of TMDs as variables and minimization of maximum structural response as an objective function while a number of constraints have been applied on TMD response and parameters.
Journal ArticleDOI
Neural methods for antenna array signal processing: a review
TL;DR: The neural method is a powerful nonlinear adaptive approach in various signal-processing scenarios, especially suitable for real-time application and hardware implementation and serves as a tutorial to the neural method for antenna array signal processing.
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