Analyzing Evolutionary Algorithms: The Computer Science Perspective (Natural Computing Series) - Softcover

Jansen, Thomas

Softcover

ISBN 10: 3642436013 ISBN 13: 9783642436017

Publisher: Springer, 2015

Synopsis

Evolutionary algorithms is a class of randomized heuristics inspired by natural evolution. They are applied in many different contexts, in particular in optimization, and analysis of such algorithms has seen tremendous advances in recent years.

In this book the author provides an introduction to the methods used to analyze evolutionary algorithms and other randomized search heuristics. He starts with an algorithmic and modular perspective and gives guidelines for the design of evolutionary algorithms. He then places the approach in the broader research context with a chapter on theoretical perspectives. By adopting a complexity-theoretical perspective, he derives general limitations for black-box optimization, yielding lower bounds on the performance of evolutionary algorithms, and then develops general methods for deriving upper and lower bounds step by step. This main part is followed by a chapter covering practical applications of these methods.

The notational and mathematical basics are covered in an appendix, the results presented are derived in detail, and each chapter ends with detailed comments and pointers to further reading. So the book is a useful reference for both graduate students and researchers engaged with the theoretical analysis of such algorithms.

About this title

About the Author

The author lectured and researched in the Technische Universität Dortmund for 9 years after his PhD, and he is now the Stokes College Lecturer in the Department of Computer Science in University College Cork. He has tested the book content in his own lectures at these universities, and he has been invited to run the tutorial on this subject at the main international conference on evolutionary computing, GECCO.

 

From the Back Cover

Evolutionary algorithms is a class of randomized heuristics inspired by natural evolution. They are applied in many different contexts, in particular in optimization, and analysis of such algorithms has seen tremendous advances in recent years.

In this book the author provides an introduction to the methods used to analyze evolutionary algorithms and other randomized search heuristics. He starts with an algorithmic and modular perspective and gives guidelines for the design of evolutionary algorithms. He then places the approach in the broader research context with a chapter on theoretical perspectives. By adopting a complexity-theoretical perspective, he derives general limitations for black-box optimization, yielding lower bounds on the performance of evolutionary algorithms, and then develops general methods for deriving upper and lower bounds step by step. This main part is followed by a chapter covering practical applications of these methods.

The notational and mathematical basics are covered in an appendix, the results presented are derived in detail, and each chapter ends with detailed comments and pointers to further reading. So the book is a useful reference for both graduate students and researchers engaged with the theoretical analysis of such algorithms.

Edition details

• Publisher: Springer
• Publication date: 2015
• Language: English
• ISBN 10: 3642436013
• ISBN 13: 9783642436017
• Binding: Paperback
• Number of pages: 268