Evolutionary Algorithms in Dynamic Optimization Problems: Studies on Memory, Diversity and Prediction - Brossura
Sinossi
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.
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L'autore
Anabela Simões, Ph.D.: studied Computer Science at the Univ. of Coimbra, where she obtained her B.Sc, M.Sc and Ph.D. She is Assistant Professor of the Coimbra Institute of Engineering and Senior Researcher at the Centre for Informatics and Systems of the University of Coimbra. She is author of several papers published in international conferences.
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Evolutionary Algorithms in Dynamic Optimization Problems
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LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10: 3846505986
ISBN 13: 9783846505984
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Taschenbuch. Condizione: 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. Codice articolo 9783846505984
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Evolutionary Algorithms in Dynamic Optimization Problems
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Evolutionary Algorithms in Dynamic Optimization Problems | Studies on Memory, Diversity and Prediction
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LAP LAMBERT Academic Publishing, 2011
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Taschenbuch. Condizione: 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. Codice articolo 106794084
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Evolutionary Algorithms in Dynamic Optimization Problems
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LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10: 3846505986
ISBN 13: 9783846505984
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Taschenbuch. Condizione: 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. Codice articolo 9783846505984
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Evolutionary Algorithms in Dynamic Optimization Problems : Studies on Memory, Diversity and Prediction
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LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846505986
ISBN 13: 9783846505984
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Taschenbuch. Condizione: 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. Codice articolo 9783846505984
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Evolutionary Algorithms in Dynamic Optimization Problems: Studies on Memory, Diversity and Prediction
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ISBN 10: 3846505986
ISBN 13: 9783846505984
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