Evolutionary computation dynamic uncertain (25 résultats)

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Taschenbuch. Etat : Neu. Evolutionary Computation in Dynamic and Uncertain Environments | Shengxiang Yang (u. a.) | Taschenbuch | Studies in Computational Intelligence | xxiii | Englisch | 2010 | Springer | EAN 9783642080654 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Evolutionary computation is a class of problem optimization methodology with the inspiration from the natural evolution of species. In nature, the population of a species evolves by means of selection and variation. These two principles of natural evolution form the fundamental of evolutionary - gorithms (EAs). During the past several decades, EAs have been extensively studied by the computer science and arti cial intelligence communities. As a classofstochasticoptimizationtechniques,EAscanoftenoutperformclassical optimization techniques for di cult real world problems. Due to the ease of use and robustness, EAs have been applied to a wide variety of optimization problems. Most of these optimization problems ta- led are stationary and deterministic. However, many real-world optimization problems are subjected to dynamic and uncertain environments that are often impossible to avoid in practice. For example, the tness function is uncertain or noisy as a result of simulation errors, measurement errors or approximation errors. In addition, the design variables or environmental conditions may also perturb or change over time. For these dynamic and uncertain optimization problems, the objective of the EA is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic en- ronments, or to nd a robust solution that operates optimally in the presence of uncertainties. This poses serious challenges to classical optimization te- niques and conventional EAs as well. However, conventional EAs with proper enhancements are still good tools of choice for optimization problems in - namic and uncertain environments.…

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Evolutionary computation is a class of problem optimization methodology with the inspiration from the natural evolution of species. In nature, the population of a species evolves by means of selection and variation. These two principles of natural evolution form the fundamental of evolutionary - gorithms (EAs). During the past several decades, EAs have been extensively studied by the computer science and arti cial intelligence communities. As a classofstochasticoptimizationtechniques,EAscanoftenoutperformclassical optimization techniques for di cult real world problems. Due to the ease of use and robustness, EAs have been applied to a wide variety of optimization problems. Most of these optimization problems ta- led are stationary and deterministic. However, many real-world optimization problems are subjected to dynamic and uncertain environments that are often impossible to avoid in practice. For example, the tness function is uncertain or noisy as a result of simulation errors, measurement errors or approximation errors. In addition, the design variables or environmental conditions may also perturb or change over time. For these dynamic and uncertain optimization problems, the objective of the EA is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic en- ronments, or to nd a robust solution that operates optimally in the presence of uncertainties. This poses serious challenges to classical optimization te- niques and conventional EAs as well. However, conventional EAs with proper enhancements are still good tools of choice for optimization problems in - namic and uncertain environments.…

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. State of the art of evolutionary algorithms in dynamic and uncertain environmentsThis book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact .…

Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. State of the art of evolutionary algorithms in dynamic and uncertain environmentsState of the art of evolutionary algorithms in dynamic and uncertain environmentsIncludes supplementary material: sn.pub/extrasThis book compile.…

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums. 632 pp. Englisch.…

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums. 632 pp. Englisch.…

Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Evolutionary computation is a class of problem optimization methodology with the inspiration from the natural evolution of species. In nature, the population of a species evolves by means of selection and variation. These two principles of natural evolution form the fundamental of evolutionary - gorithms (EAs). During the past several decades, EAs have been extensively studied by the computer science and arti cial intelligence communities. As a classofstochasticoptimizationtechniques,EAscanoftenoutperformclassical optimization techniques for di cult real world problems. Due to the ease of use and robustness, EAs have been applied to a wide variety of optimization problems. Most of these optimization problems ta- led are stationary and deterministic. However, many real-world optimization problems are subjected to dynamic and uncertain environments that are often impossible to avoid in practice. For example, the tness function is uncertain or noisy as a result of simulation errors, measurement errors or approximation errors. In addition, the design variables or environmental conditions may also perturb or change over time. For these dynamic and uncertain optimization problems, the objective of the EA is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic en- ronments, or to nd a robust solution that operates optimally in the presence of uncertainties. This poses serious challenges to classical optimization te- niques and conventional EAs as well. However, conventional EAs with proper enhancements are still good tools of choice for optimization problems in - namic and uncertain environments.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 632 pp. Englisch. …

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Evolutionary computation is a class of problem optimization methodology with the inspiration from the natural evolution of species. In nature, the population of a species evolves by means of selection and variation. These two principles of natural evolution form the fundamental of evolutionary - gorithms (EAs). During the past several decades, EAs have been extensively studied by the computer science and arti cial intelligence communities. As a classofstochasticoptimizationtechniques,EAscanoftenoutperformclassical optimization techniques for di cult real world problems. Due to the ease of use and robustness, EAs have been applied to a wide variety of optimization problems. Most of these optimization problems ta- led are stationary and deterministic. However, many real-world optimization problems are subjected to dynamic and uncertain environments that are often impossible to avoid in practice. For example, the tness function is uncertain or noisy as a result of simulation errors, measurement errors or approximation errors. In addition, the design variables or environmental conditions may also perturb or change over time. For these dynamic and uncertain optimization problems, the objective of the EA is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic en- ronments, or to nd a robust solution that operates optimally in the presence of uncertainties. This poses serious challenges to classical optimization te- niques and conventional EAs as well. However, conventional EAs with proper enhancements are still good tools of choice for optimization problems in - namic and uncertain environments.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 632 pp. Englisch.…

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