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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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Focusing coherent light through opaque strongly scattering media
Ivo M. Vellekoop,Allard Mosk +1 more
TL;DR: Focusing of coherent light through opaque scattering materials by control of the incident wavefront with a brightness up to a factor of 1000 higher than the brightness of the normal diffuse transmission is reported.
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SMS-EMOA : Multiobjective selection based on dominated hypervolume
TL;DR: A steady-state EMOA is proposed that features a selection operator based on the hypervolume measure combined with the concept of non-dominated sorting, thereby focussing on interesting regions of the Pareto front.
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Tutorial on agent-based modelling and simulation
TL;DR: A brief introduction to ABMS is provided, the main concepts and foundations are illustrated, some recent applications across a variety of disciplines are discussed, and methods and toolkits for developing agent models are identified.
Journal ArticleDOI
Community detection in complex networks using extremal optimization.
Jordi Duch,Alex Arenas +1 more
TL;DR: The method outperforms the optimal modularity found by the existing algorithms in the literature and is feasible to be used for the accurate identification of community structure in large complex networks.
Reinforcement Learning in Robotics: A Survey.
Jens Kober,Jan Peters +1 more
TL;DR: A survey of work in reinforcement learning for behavior generation in robots can be found in this article, where the authors highlight key challenges in robot reinforcement learning as well as notable successes and discuss the role of algorithms, representations and prior knowledge in achieving these successes.
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