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Autor(en): 
  • Z.B. Zabinsky
  • Stochastic Adaptive Search for Global Optimization 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2003  
    Genre:  Schulbücher 
     
    C / Calculus of variations / Calculus of Variations and Optimal Control; Optimization / Calculus of Variations and Optimization / Combinatorics / Combinatorics & graph theory / computer science / Computers / Discrete Mathematics / Mathematical optimization / Mathematical theory of computation / Mathematics and Statistics / Optimization / Theory of Computation
    ISBN:  9781402075261 
    EAN-Code: 
    9781402075261 
    Verlag:  Springer Nature EN 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 234 mm / B 156 mm / D 19 mm 
    Gewicht:  1160 gr 
    Seiten:  224 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    The field of global optimization has been developing at a rapid pace. There is a journal devoted to the topic, as well as many publications and notable books discussing various aspects of global optimization. This book is intended to complement these other publications with a focus on stochastic methods for global optimization. Stochastic methods, such as simulated annealing and genetic algo­ rithms, are gaining in popularity among practitioners and engineers be­ they are relatively easy to program on a computer and may be cause applied to a broad class of global optimization problems. However, the theoretical performance of these stochastic methods is not well under­ stood. In this book, an attempt is made to describe the theoretical prop­ erties of several stochastic adaptive search methods. Such a theoretical understanding may allow us to better predict algorithm performance and ultimately design new and improved algorithms. This book consolidates a collection of papers on the analysis and de­ velopment of stochastic adaptive search. The first chapter introduces random search algorithms. Chapters 2-5 describe the theoretical anal­ ysis of a progression of algorithms. A main result is that the expected number of iterations for pure adaptive search is linear in dimension for a class of Lipschitz global optimization problems. Chapter 6 discusses algorithms, based on the Hit-and-Run sampling method, that have been developed to approximate the ideal performance of pure random search. The final chapter discusses several applications in engineering that use stochastic adaptive search methods.
      
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