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Evolutionary Optimization in Dynamic Environments (Genetic Algorithms and Evolutionary Computation) [Hardcover]

J�rgen Branke (Author)

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Book Description

Publication Date: December 1, 2001 | ISBN-10: 0792376315 | ISBN-13: 978-0792376316 | Edition: 1

Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural evolution is a dynamic process in a changing environment, EAs are also well suited to dynamic optimization problems. Evolutionary Optimization in Dynamic Environments is the first comprehensive work on the application of EAs to dynamic optimization problems. It provides an extensive survey on research in the area and shows how EAs can be successfully used to continuously and efficiently adapt a solution to a changing environment, find a good trade-off between solution quality and adaptation cost, find robust solutions whose quality is insensitive to changes in the environment, find flexible solutions which are not only good but that can be easily adapted when necessary. All four aspects are treated in this book, providing a holistic view on the challenges and opportunities when applying EAs to dynamic optimization problems. The comprehensive and up-to-date coverage of the subject, together with details of latest original research, makes Evolutionary Optimization in Dynamic Environments an invaluable resource for researchers and professionals who are dealing with dynamic and stochastic optimization problems, and who are interested in applying local search heuristics, such as evolutionary algorithms.

#outer_postBodyPS { display: none; } #psGradient { display: none; } #psPlaceHolder { display: none; } #psExpand { display: none; } Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural evolution is a dynamic process in a changing environment, EAs are also well suited to dynamic optimization problems. Evolutionary Optimization in Dynamic Environments is the first comprehensive work on the application of EAs to dynamic optimization problems. It provides an extensive survey on research in the area and shows how EAs can be successfully used to continuously and efficiently adapt a solution to a changing environment, find a good trade-off between solution quality and adaptation cost, find robust solutions whose quality is insensitive to changes in the environment, find flexible solutions which are not only good but that can be easily adapted when necessary. All four aspects are treated in this book, providing a holistic view on the challenges and opportunities when applying EAs to dynamic optimization problems. The comprehensive and up-to-date coverage of the subject, together with details of latest original research, makes Evolutionary Optimization in Dynamic Environments an invaluable resource for researchers and professionals who are dealing with dynamic and stochastic optimization problems, and who are interested in applying local search heuristics, such as evolutionary algorithms.

Product Details

Hardcover: 224 pages
Publisher: Springer; 1 edition (December 1, 2001)
Language: English
ISBN-10: 0792376315
ISBN-13: 978-0792376316
Product Dimensions: 9.4 x 6.4 x 0.6 inches
Shipping Weight: 1.1 pounds (View shipping rates and policies)
Amazon Best Sellers Rank: #5,043,644 in Books (See Top 100 in Books)

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Inside This Book (learn more)

First Sentence:
Evolutionary algorithms, are randomized heuristic search methods based on the principles of natural evolution, or more specifically, sic Darwin's theory of survival of the fittest. Read the first page

Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
scout population, offline error, moving peaks benchmark, triallelic scheme, oscillating peaks, disturbed evaluations, dominance scheme, effective fitness, mutation step size, dominance change mechanism, offline performance, random immigrants, linear ranking selection, old optimum, peak cover, fitness proportional selection, scouts approach, schedule builder, pareto optimal front, generational reproduction, noisy functions, mean tardiness, search population, allele value, dynamic optimization problems

Key Phrases - Capitalized Phrases (CAPs): (learn more)
Guided Multi-Objective Evolutionary Algorithm

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Citations (learn more)

This book cites 25 books:

Evolutionary Computation by A.E. Eiben on 4 pages
Evolutionary Computation by Kenneth A. de De Jong in Back Matter (1), and Back Matter (2)
Intelligent Hybrid Systems by Suran Goonatilake in Back Matter
The Practical Handbook of Genetic Algorithms: Applications, Second Edition by Lance D. Chambers in Back Matter
Manufacturing Science (Ellis Horwood series in engineering science) by A. Ghosh in Back Matter

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16 books cite this book:

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Books on Related Topics (learn more)

How to Solve It by Zbigniew Michalewicz

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Introduction to Evolutionary Computing by Agoston E. Eiben

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Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz

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Evolutionary Computation 1 by Thomas Back

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