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Analyzing Evolutionary Algorithms The Computer Science Perspective /
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 t...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2013.
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| Edición: | 1st ed. 2013. |
| Colección: | Natural Computing Series,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-642-17339-4 | ||
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| 100 | 1 | |a Jansen, Thomas. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Analyzing Evolutionary Algorithms |h [electronic resource] : |b The Computer Science Perspective / |c by Thomas Jansen. |
| 250 | |a 1st ed. 2013. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2013. | |
| 300 | |a X, 258 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Natural Computing Series, |x 2627-6461 | |
| 505 | 0 | |a Introduction -- Evolutionary Algorithms and Other Randomized Search Heuristics -- Theoretical Perspectives on Evolutionay Algorithms -- General Limits in Black-Box Optimization -- Methods for the Analysis of Evolutionary Algorithms -- Selected Topics in the Analysis of Evolutionary Algorithms -- App. A, Landau Notation -- App. B, Tail Estimations -- App. C, Martingales and Applications. | |
| 520 | |a 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. . | ||
| 650 | 0 | |a Computer science. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Theory of Computation. |
| 650 | 2 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Optimization. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642173387 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642173400 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642436017 |
| 830 | 0 | |a Natural Computing Series, |x 2627-6461 | |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||