Evolutionary Algorithms, Swarm Dynamics and Complex Networks
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106,99 €
Methodology, Perspectives and Implementation, Emergence, Complexity and Computation 26
| ISBN: |
3662556618 |
| ISBN 13: |
9783662556610 |
| Herausgeber: |
Ivan Zelinka/Guanrong Chen |
| Verlag: |
Springer Verlag GmbH |
| Umfang: |
xxii, 312 S., 39 s/w Illustr., 155 farbige Illustr., 312 p. 194 illus., 155 illus. in color. |
| Erscheinungsdatum: |
11.12.2017 |
| Auflage: |
1/2018 |
| Produktform: |
Gebunden/Hardback |
| Einband: |
GEB |
Evolutionary algorithms constitute a class of well-known algorithms, which are designed based on the Darwinian theory of evolution and Mendelian theory of heritage. They are partly based on random and partly based on deterministic principles. Due to this nature, it is challenging to predict and control its performance in solving complex nonlinear problems. Recently, the study of evolutionary dynamics is focused not only on the traditional investigations but also on the understanding and analyzing new principles, with the intention of controlling and utilizing their properties and performances toward more effective real-world applications. In this book, based on many years of intensive research of the authors, is proposing novel ideas about advancing evolutionary dynamics towards new phenomena including many new topics, even the dynamics of equivalent social networks. In fact, it includes more advanced complex networks and incorporates them with the CMLs (coupled map lattices), which are usually used for spatiotemporal complex systems simulation and analysis, based on the observation that chaos in CML can be controlled, so does evolution dynamics. All the chapter authors are, to the best of our knowledge, originators of the ideas mentioned above and researchers on evolutionary algorithms and chaotic dynamics as well as complex networks, who will provide benefits to the readers regarding modern scientific research on related subjects.
Beschreibung
This book puts forward novel ideas, advancing evolutionary dynamics to address new phenomena and new topics, even the dynamics of equivalent social networks. It demonstrates that evolutionary algorithms can be understood just like dynamical systems with feedback. Evolutionary algorithms constitute a class of well-known algorithms that are based on the Darwinian theory of evolution and Mendelian theory of heritage. They are partly based on random and partly on deterministic principles. Recently, the study of evolutionary dynamics has focused not only on the traditional investigations but also on understanding and analyzing new principles, the goal being to hone and harness their qualities and performance for more effective real-world applications. Thus, at least in theory, all engineering control methods can be applied. All of these ideas are illustrated and discussed in the book's respective chapters. All the chapter authors are originators of the ideas mentioned above and researchers intensively engaged in evolutionary algorithms, chaotic dynamics and complex networks, and offer readers essential insights into the latest scientific research on these subjects.
