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Autor(en): 
  • Kay Chen Tan
  • Chi-Keong Goh
  • Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  März 2009  
    Genre:  EDV / Informatik 
     
    Applied mathematics / Artificial Intelligence / C / Computer-aided engineering / Computer-Aided Engineering (CAD, CAE) and Design / engineering / Engineering mathematics / Künstliche Intelligenz
    ISBN:  9783540959755 
    EAN-Code: 
    9783540959755 
    Verlag:  Springer EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  #186 - Studies in Computational Intelligence  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  1270 gr 
    Seiten:  271 
    Illustration:  XI, 271 p. 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined.

    The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties.

      



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