Skip to main content

Parallel Genetic Algorithms

Theory and Real World Applications

  • Book
  • © 2011


  • Presents theory and applications of Parallel Genetic Algorithms
  • Written by leading experts in this field
  • State-of-the-Art book

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

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

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.



Similar content being viewed by others


Table of contents (8 chapters)

  1. Introduction

  2. Characterization of Parallel Genetic Algorithms

  3. Applications of Parallel Genetic Algorithms


From the reviews:

“The book is clearly addressed at the practitioner in the field and at people interested in applying parallel evolutionary algorithms to some specific real world problem. … For practitioners, the book offers four very concrete and precise case studies, an introduction to a library that facilitates implementation and a thorough and practical guide towards presenting their own results in scientific publications. The book extends well beyond a summary of the state of the art and contains a large amount of original research results.” (Thomas Jansen, Zentralblatt MATH, Vol. 1232, 2012)

Authors and Affiliations

  • E.T.S.I. Informática, University of Málaga, Málaga, Spain

    Gabriel Luque, Enrique Alba

Bibliographic Information

  • Book Title: Parallel Genetic Algorithms

  • Book Subtitle: Theory and Real World Applications

  • Authors: Gabriel Luque, Enrique Alba

  • Series Title: Studies in Computational Intelligence

  • DOI:

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2011

  • Hardcover ISBN: 978-3-642-22083-8Published: 15 June 2011

  • Softcover ISBN: 978-3-642-26868-7Published: 03 August 2013

  • eBook ISBN: 978-3-642-22084-5Published: 15 June 2011

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 172

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

Publish with us