Parallel Genetic Algorithms

Theory and Real World Applications

  • Gabriel Luque
  • Enrique Alba

Part of the Studies in Computational Intelligence book series (SCI, volume 367)

Table of contents

  1. Front Matter
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Gabriel Luque, Enrique Alba
      Pages 3-13
    3. Gabriel Luque, Enrique Alba
      Pages 15-30
  3. Characterization of Parallel Genetic Algorithms

    1. Front Matter
      Pages 53-53
    2. Gabriel Luque, Enrique Alba
      Pages 55-71
  4. Applications of Parallel Genetic Algorithms

    1. Front Matter
      Pages 73-73
    2. Gabriel Luque, Enrique Alba
      Pages 75-89
    3. Gabriel Luque, Enrique Alba
      Pages 91-114
    4. Gabriel Luque, Enrique Alba
      Pages 115-134
    5. Gabriel Luque, Enrique Alba
      Pages 135-147
  5. Back Matter

About this book


This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers can learn how to solve complex tasks by reducing their high computational times. Dealing with two scientific fields (parallelism and GAs) is always difficult, and the book seeks at gracefully introducing from basic concepts to advanced topics.


The presentation is structured in three parts. The first one is targeted to the algorithms themselves, discussing their components, the physical parallelism, and best practices in using and evaluating them. A second part deals with the theory for pGAs, with an eye on theory-to-practice issues. A final third part offers a very wide study of pGAs as practical problem solvers, addressing domains such as natural language processing, circuits design, scheduling, and genomics.


This volume will be helpful both for researchers and practitioners. The first part shows pGAs to either beginners and mature researchers looking for a unified view of the two fields: GAs and parallelism. The second part partially solves (and also opens) new investigation lines in theory of pGAs. The third part can be accessed independently for readers interested in applications. The result is an excellent source of information on the state of the art and future developments in parallel GAs.




Computational Intelligence Genetic Algorithms Parallel Genetic Algorithms

Authors and affiliations

  • Gabriel Luque
    • 1
  • Enrique Alba
    • 1
  1. 1.E.T.S.I. InformáticaUniversity of MálagaMálagaSpain

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-22083-8
  • Online ISBN 978-3-642-22084-5
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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