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Autor(en): 
  • Enrique Alba
  • Bernabe Dorronsoro
  • Cellular Genetic Algorithms 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Dezember 2010  
    Genre:  Schulbücher 
     
    Algorithms / Applied mathematics / B / Biomathematics / Decision Making / Genetics and Population Dynamics / Management decision making / Management of specific areas
    ISBN:  9781441945945 
    EAN-Code: 
    9781441945945 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  #42 - Operations Research/Computer Science Interfaces Series  
    Dimensionen:  H 235 mm / B 155 mm / D 14 mm 
    Gewicht:  409 gr 
    Seiten:  248 
    Illustration:  XIV, 248 p. 72 illus., schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    CELLULAR GENETIC ALGORITHMS defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications. These methods can include local search (memetic algorithms), cooperation, parallelism, multi-objective, estimations of distributions, and self-adaptive ideas to extend their applicability.

    The methods are benchmarked against well-known metaheutistics like Genetic Algorithms, Tabu Search, heterogeneous GAs, Estimation of Distribution Algorithms, etc. Also, a publicly available software tool is offered to reduce the learning curve in applying these techniques. The three final chapters will use the classic problem of "vehicle routing" and the hot topics of "ad-hoc mobile networks" and "DNA genome sequencing" to clearly illustrate and demonstrate the power and utility of these algorithms.

      



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