Advertisement

Parallel and Distributed Computational Intelligence

  • Francisco Fernández de Vega
  • Erick Cantú-Paz

Part of the Studies in Computational Intelligence book series (SCI, volume 269)

Table of contents

  1. Front Matter
  2. Francisco Fernández de Vega, Erick Cantú-Paz
    Pages 1-9
  3. Xavier Llorà, Abhishek Verma, Roy H. Campbell, David E. Goldberg
    Pages 11-41
  4. Juan Luis Jimenez Laredo, Juan Julian Merelo Guervos, Pedro Angel Castillo Valdivieso
    Pages 43-62
  5. Nate Cole, Travis Desell, Daniel Lombraña González, Francisco Fernández de Vega, Malik Magdon-Ismail, Heidi Newberg et al.
    Pages 63-90
  6. Alexander Mendiburu, Jose Miguel-Alonso, Jose A. Lozano
    Pages 143-163
  7. Ignacio Robles, Rafael Alcalá, José M. Benítez, Francisco Herrera
    Pages 235-261
  8. José L. Risco-Martín, David Atienza, J. Ignacio Hidalgo, Juan Lanchares
    Pages 263-290
  9. Alexandru-Adrian Tantar, Nouredine Melab, El-Ghazali Talbi
    Pages 291-319
  10. J. L. Guisado, F. Jiménez-Morales, J. M. Guerra, F. Fernández de Vega, K. A. Iskra, P. M. A. Sloot et al.
    Pages 321-347
  11. Back Matter

About this book

Introduction

The growing success of biologically inspired algorithms in solving large and complex problems has spawned many interesting areas of research. Over the years, one of the mainstays in bio-inspired research has been the exploitation of parallel and distributed environments to speedup computations and to enrich the algorithms. From the early days of research on bio-inspired algorithms, their inherently parallel nature was recognized and different parallelization approaches have been explored. Parallel algorithms promise reductions in execution time and open the door to solve increasingly larger problems. But parallel platforms also inspire new bio-inspired parallel algorithms that, while similar to their sequential counterparts, explore search spaces differently and offer improvements in solution quality.

The objective in editing this book was to assemble a sample of the best work in parallel and distributed biologically inspired algorithms. The editors invited researchers in different domains to submit their work. They aimed to include diverse topics to appeal to a wide audience. Some of the chapters summarize work that has been ongoing for several years, while others describe more recent exploratory work. Collectively, these works offer a global snapshot of the most recent efforts of bioinspired algorithms’ researchers aiming at profiting from parallel and distributed computer architectures—including GPUs, Clusters, Grids, volunteer computing and p2p networks as well as multi-core processors. This volume will be of value to a wide set of readers, including, but not limited to specialists in Bioinspired Algorithms, Parallel and Distributed Computing, as well as computer science students trying to figure out new paths towards the future of computational intelligence.

Keywords

algorithm algorithms bioinformatics cluster computational intelligence data mining evolutionary algorithm genetic algorithms intelligence modeling multi-core multi-objective optimization optimization parallel computing simulation

Editors and affiliations

  • Francisco Fernández de Vega
    • 1
  • Erick Cantú-Paz
    • 2
  1. 1.University of ExtremaduraMeridaSpain
  2. 2.Yahoo! Inc.SunnyvaleUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-10675-0
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-10674-3
  • Online ISBN 978-3-642-10675-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site