Evolutionary Algorithms in Dynamic Optimization Problems | Studies on Memory, Diversity and Prediction

Anabela Simões

ISBN 10: 3846505986 / ISBN 13: 9783846505984

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

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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Bibliographic Details

• Title: Evolutionary Algorithms in Dynamic Optimization Problems | Studies on Memory, Diversity and Prediction
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3846505986
• ISBN 13: 9783846505984
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 14D millimeters 220 millimeters width by 150 millimeters height by 14 millimeters depth
• Weight: 340 grams
• Seller catalogs:
  • Bücher

About this title

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.

About the Author

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.