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| − | | | + | |Abstract=The book develops a unifying perspective on how learning influences evolutionary dynamics which for the first time explains seemingly contradicting results from the literature. It also reveals formerly unknown facets of the complex interplay of the two major biological adaptation mechanisms. The results apply to models from computational intelligence and evolutionary biology alike, thus contribute to the transfer of principles of biological adaptation to digital processing system. |
| − | |Forschungsgruppe= | + | |ISBN=978-3-86644-247-4 |
| + | |Link=http://uvka.ubka.uni-karlsruhe.de/shop/download/1000008384 | ||
| + | |Projekt=EVOLEARN | ||
| + | |Forschungsgruppe=Effiziente Algorithmen | ||
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| + | {{Forschungsgebiet Auswahl | ||
| + | |Forschungsgebiet=Artificial Life | ||
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| + | {{Forschungsgebiet Auswahl | ||
| + | |Forschungsgebiet=Computational Biology | ||
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| + | {{Forschungsgebiet Auswahl | ||
| + | |Forschungsgebiet=Evolutionäre Algorithmen | ||
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| + | {{Forschungsgebiet Auswahl | ||
| + | |Forschungsgebiet=Evolutionäre Optimierung veränderlicher Probleme | ||
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Aktuelle Version vom 24. September 2009, 21:12 Uhr
Datum: 29. Februar 2008
PhD thesis at the Universität Karlsruhe (TH), Fakultät für Wirtschaftswissenschaften
Referent(en): Prof. Dr. Hartmut Schmeck / Prof. Dr. Xin Yao
BibTeX
Kurzfassung
The book develops a unifying perspective on how learning influences evolutionary dynamics which for the first time explains seemingly contradicting results from the literature. It also reveals formerly unknown facets of the complex interplay of the two major biological adaptation mechanisms. The results apply to models from computational intelligence and evolutionary biology alike, thus contribute to the transfer of principles of biological adaptation to digital processing system.
ISBN: 978-3-86644-247-4
Weitere Informationen unter: Link
Evolutionäre Algorithmen, Evolutionäre Optimierung veränderlicher Probleme, Computational Biology, Artificial Life