Introduction to evolutionary computing [electronic resource] / A.E. Eiben, J.E. Smith.
Eiben, Agoston E., author.; Smith, J. E. (James E.), 1964- author.
2015
QA76.618 .E43 2015eb
Available Online

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Title Introduction to evolutionary computing [electronic resource] / A.E. Eiben, J.E. Smith.
Author Eiben, Agoston E., author.
Edition Second edition.
ISBN 9783662448748 (electronic book)
3662448742 (electronic book)
9783662448731
DOI https://doi.org/10.1007/978-3-662-44874-8
Published Berlin : Springer, 2015.
Language English
Description 1 online resource (xi, 287 pages) : illustrations.
Item Number 10.1007/978-3-662-44874-8 doi
Call Number QA76.618 .E43 2015eb
Dewey Decimal Classification 006.3/823
Summary The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed July 6, 2015).
Added Author Smith, J. E. (James E.), 1964- author.
Series Natural computing series.
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Problems to Be Solved
Evolutionary Computing: The Origins
What Is an Evolutionary Algorithm?
Representation, Mutation, and Recombination
Fitness, Selection, and Population Management
Popular Evolutionary Algorithm Variants
Hybridisation with Other Techniques: Memetic Algorithms
Nonstationary and Noisy Function Optimisation
Multiobjective Evolutionary Algorithms
Constraint Handling
Interactive Evolutionary Algorithms
Coevolutionary Systems
Theory
Evolutionary Robotics
Parameters and Parameter Tuning
Parameter Control
Working with Evolutionary Algorithms
References.

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