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

J�rgen Branke (Author)

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Product details

Hardcover: 224 pages
Publisher: Springer (31 Dec 2001)
Language English
ISBN-10: 0792376315
ISBN-13: 978-0792376316
Product Dimensions: 2.3 x 1.8 x 0.2 cm
Amazon Bestsellers Rank: 2,375,373 in Books (See Top 100 in Books)
- #86 in Books > Computers & Internet > Computer Science > Algorithms > Genetic

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Product Description

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.

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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

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