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Grundlagen genetischer und evolutionärer Algorithmen

  • 2024
  • OriginalPaper
  • Chapter
Published in:
Author
Klaus Mainzer
Published in
Philosophisches Handbuch Künstliche Intelligenz
Publisher
Springer Fachmedien Wiesbaden

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Zusammenfassung

Die komplexen Datenmengen der Genombestimmung erfordern nicht nur schnelle und leistungsfähige Computer. Mit Machine Learning, statistischen und kausalen Lernalgorithmen werden Sequenzanalysen, Struktur- und Funktionsbestimmungen von Proteinen erste möglich. Unter dieser Voraussetzung eröffnen sich neue Perspektiven, um Krankheiten frühzeitig zu erkennen, aber auch virale Pandemien zu bekämpfen. Dabei werden RNA- und DNA-Strukturen als formale Sprachsysteme aufgefasst, die in lernenden Automaten und Maschinen verarbeitet werden. Ebenso ist die komplexe molekulare Evolution des Lebens nur noch durch die Unterstützung von Methoden des Maschine Learning zu entschlüsseln. Bioinformatik und Systembiologie liefern damit die entscheidenden Methoden der theoretischen Biologie. Die philosophische Reflexion ihrer Modelle ist mit den Grundlagen des Machine Learning und der Künstlichen Intelligenz eng verbunden.

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previous chapter Grundlagen zellulärer Automaten
next chapter Grundlagenfragen der Neurocomputation und Neurokognition
Title
Grundlagen genetischer und evolutionärer Algorithmen
Author
Klaus Mainzer
Copyright Year
2024
Book
Philosophisches Handbuch Künstliche Intelligenz

Print ISBN: 978-3-658-19605-9

Electronic ISBN: 978-3-658-19606-6

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-658-19606-6_16
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