Verwandte Artikel zu Mathematical Foundations of Nature-Inspired Algorithms...
Inhaltsangabe
This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Reseña del editor
This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
- VerlagSpringer
- Erscheinungsdatum2019
- ISBN 10 3030169359
- ISBN 13 9783030169350
- EinbandTapa blanda
- SpracheEnglisch
- Auflage1
- Anzahl der Seiten120
- Kontakt zum HerstellerNicht verfügbar
Neu kaufen
Diesen Artikel anzeigen
EUR 57,15
Gratis für den Versand innerhalb von/der Deutschland
Versandziele, Kosten & Dauer
Suchergebnisse für Mathematical Foundations of Nature-Inspired Algorithms...
Foto des Verkäufers
Mathematical Foundations of Nature-Inspired Algorithms
Verlag:
Springer International Publishing, 2019
ISBN 10: 3030169359
ISBN 13: 9783030169350
Neu
Softcover
Anbieter: moluna, Greven, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Analyzes nature-inspired algorithmsProvides a unified framework of mathematical analysis for convergence and stabilityFeatures methods and techniques for selecting specific algorithms Analyzes nature-inspired algorithmsPro. Bestandsnummer des Verkäufers 279701559
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Mathematical Foundations of Nature-Inspired Algorithms
Verlag:
Springer International Publishing Mai 2019, 2019
ISBN 10: 3030169359
ISBN 13: 9783030169350
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms. 120 pp. Englisch. Bestandsnummer des Verkäufers 9783030169350
Anzahl: 2 verfügbar
Foto des Verkäufers
Mathematical Foundations of Nature-Inspired Algorithms
ISBN 10: 3030169359
ISBN 13: 9783030169350
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms. Bestandsnummer des Verkäufers 9783030169350
Anzahl: 1 verfügbar
Foto des Verkäufers
Mathematical Foundations of Nature-Inspired Algorithms
ISBN 10: 3030169359
ISBN 13: 9783030169350
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch. Bestandsnummer des Verkäufers 9783030169350
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Mathematical Foundations of Nature-Inspired Algorithms (SpringerBriefs in Optimization)
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. In. Bestandsnummer des Verkäufers ria9783030169350_new
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Mathematical Foundations of Nature-Inspired Algorithms
Anbieter: Chiron Media, Wallingford, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
PF. Zustand: New. Bestandsnummer des Verkäufers 6666-IUK-9783030169350
Anzahl: 10 verfügbar
Foto des Verkäufers
Mathematical Foundations of Nature-inspired Algorithms
Anbieter: GreatBookPrices, Columbia, MD, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: As New. Unread book in perfect condition. Bestandsnummer des Verkäufers 35369970
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Mathematical Foundations of Nature-inspired Algorithms
Anbieter: GreatBookPrices, Columbia, MD, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers 35369970-n
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Mathematical Foundations of Nature-inspired Algorithms
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers 35369970-n
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Mathematical Foundations of Nature-Inspired Algorithms (SpringerBriefs in Optimization)
Anbieter: California Books, Miami, FL, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers I-9783030169350
Anzahl: Mehr als 20 verfügbar