Isbn: 9781461353973 - noisy optimization with evolution strategies (genetic algorithms and evolutionary computation, band 8) (2 Ergebnisse)

ISBN: 
Mit der Detailsuche verfeinern

Optimieren Sie Ihre Suche

  • Bücher (2)

  • Neu (2)

bis

Benutzerdefinierte Preisspanne (EUR)

bis

  • Sprache: Englisch

    Verlag: Springer, 2012

    1461353971 / 9781461353973

    • Softcover

    Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections

    Verkäufer/-in mit 5 Sternen
    Verkäufer/-in kontaktieren

    Zustand: Neu

    EUR 129,67

    EUR 11,05 Versand 
    Versand von Vereinigtes Königreich nach USA

    Anzahl: Mehr als 20 verfügbar

    Zustand: New. In English.

  • Sprache: Englisch

    Verlag: Springer, 2012

    1461353971 / 9781461353973

    • Softcover

    Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH

    Verkäufer/-in mit 5 Sternen
    Verkäufer/-in kontaktieren

    Zustand: Neu

    EUR 114,00

    EUR 35,00 Versand 
    Versand von Deutschland nach USA

    Anzahl: 1 verfügbar

    Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, stochastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary algorithms are general, nature-inspired heuristics for numerical search and optimization that are frequently observed to be particularly robust with regard to the effects of noise.Noisy Optimization with Evolution Strategies contributes to the understanding of evolutionary optimization in the presence of noise by investigating the performance of evolution strategies, a type of evolutionary algorithm frequently employed for solving real-valued optimization problems. By considering simple noisy environments, results are obtained that describe how the performance of the strategies scales with both parameters of the problem and of the strategies considered. Such scaling laws allow for comparisons of different strategy variants, for tuning evolution strategies for maximum performance, and they offer insights and an understanding of the behavior of the strategies that go beyond what can be learned from mere experimentation.This first comprehensive work on noisy optimization with evolution strategies investigates the effects of systematic fitness overvaluation, the benefits of distributed populations, and the potential of genetic repair for optimization in the presence of noise. The relative robustness of evolution strategies is confirmed in a comparison with other direct search algorithms.Noisy Optimization with Evolution Strategies is an invaluable resource for researchers and practitioners of evolutionary algorithms.…