Evolutionary Algorithms in Dynamic Optimization Problems: Studies on Memory, Diversity and Prediction - Couverture souple

Simões, Anabela

 
9783846505984: Evolutionary Algorithms in Dynamic Optimization Problems: Studies on Memory, Diversity and Prediction
Couverture souple
ISBN 10 : 3846505986 ISBN 13 : 9783846505984
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3846505986
ISBN 13 
9783846505984
Reliure
Broché
Nombre de pages
216
Coordonnées du fabricant
non disponible
Personne responsable
KreGa Onlinehandel GmbH
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Autres éditions populaires du même titre

9786139897025: Evolutionary Algorithms for Solving Dynamic Optimization Problems

Edition présentée

ISBN 10 :  6139897025 ISBN 13 :  9786139897025
Editeur : LAP LAMBERT Academic Publishing, 2019
Couverture souple