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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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Spacecraft trajectory optimization: A review of models, objectives, approaches and solutions
TL;DR: The solving process is decomposed into four key steps of mathematical modeling of the problem, defining the objective functions, development of an approach and obtaining the solution of theproblem.
Journal ArticleDOI
Adaptive Idea Screening Using Consumers
Olivier Toubia,Laurent Flores +1 more
TL;DR: In this paper, the authors proposed a practical approach to involving consumers in idea screening, and described several idea-screening algorithms that perform this selection adaptively based on the evaluations made by previous consumers.
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The multiple server location problem
Oded Berman,Zvi Drezner +1 more
TL;DR: The Multiple Server location problem is introduced and it turns out to be a very difficult combinatorial problem when the total demand is very close to the total capacity of the servers.
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Pareto optimal sensor locations for structural identification
TL;DR: A heuristic algorithm is proposed for constructing effective Pareto optimal sensor configurations that are superior, in terms of computational efficiency and accuracy, to the Pare to sensor configurations predicted by evolutionary algorithms suitable for solving general multi-objective optimisation problems.
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