evolutionary algorithms dynamic optimization de simões anabela (6 résultats)

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Taschenbuch. Etat : Neu. Evolutionary Algorithms in Dynamic Optimization Problems | Studies on Memory, Diversity and Prediction | Anabela Simões | Taschenbuch | 216 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846505984 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores the use of Evolutionary Algorithms (EAs) in dynamic optimization problems. Evolutionary Algorithms are powerful tools for optimization problems. Nevertheless, when the problem is dynamic, the EA can face difficulties due to the convergence of the population on a specific region of the search space. Different improvements have been made to the standard EA to make it more robust in dynamic problems: the increase of diversity, the incorporation of memory or the inclusion of anticipation methods. In this book we introduce important and novel contributions to address some of the drawbacks of current approaches. First, the book describes different approaches to make memory more useful and effective, including a new algorithm that evolves the best memory size according to the moment and characteristics of the dynamic problem. Second, the book analyses the importance of the population s diversity in EAs for dynamic optimization problems, by using two different biologically inspired genetic operators. Third, different prediction techniques that allow the EA to forecast both the time of the next change and the direction of this change are introduced. 216 pp. Englisch.

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book explores the use of Evolutionary Algorithms (EAs) in dynamic optimization problems. Evolutionary Algorithms are powerful tools for optimization problems. Nevertheless, when the problem is dynamic, the EA can face difficulties due to the convergence of the population on a specific region of the search space. Different improvements have been made to the standard EA to make it more robust in dynamic problems: the increase of diversity, the incorporation of memory or the inclusion of anticipation methods. In this book we introduce important and novel contributions to address some of the drawbacks of current approaches. First, the book describes different approaches to make memory more useful and effective, including a new algorithm that evolves the best memory size according to the moment and characteristics of the dynamic problem. Second, the book analyses the importance of the population's diversity in EAs for dynamic optimization problems, by using two different biologically inspired genetic operators. Third, different prediction techniques that allow the EA to forecast both the time of the next change and the direction of this change are introduced.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 216 pp. Englisch.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book explores the use of Evolutionary Algorithms (EAs) in dynamic optimization problems. Evolutionary Algorithms are powerful tools for optimization problems. Nevertheless, when the problem is dynamic, the EA can face difficulties due to the convergence of the population on a specific region of the search space. Different improvements have been made to the standard EA to make it more robust in dynamic problems: the increase of diversity, the incorporation of memory or the inclusion of anticipation methods. In this book we introduce important and novel contributions to address some of the drawbacks of current approaches. First, the book describes different approaches to make memory more useful and effective, including a new algorithm that evolves the best memory size according to the moment and characteristics of the dynamic problem. Second, the book analyses the importance of the population s diversity in EAs for dynamic optimization problems, by using two different biologically inspired genetic operators. Third, different prediction techniques that allow the EA to forecast both the time of the next change and the direction of this change are introduced.