Information Processing with Evolutionary Algorithms

From Industrial Applications to Academic Speculations

  • Xindong Wu
  • Lakhmi Jain
  • Manuel Graña
  • Richard J. Duro
  • Alicia d’Anjou
  • Paul P. Wang

Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Elsa Fernández, Manuel Graña, Jesús Ruiz-Cabello
    Pages 61-72
  3. Ramiro Varela, Camino R. Vela, Jorge Puente, David Serrano, Ana Suárez
    Pages 74-82
  4. J. A. Becerra, J. Santos, R.J. Duro
    Pages 117-127
  5. W. Cyre
    Pages 129-142
  6. Oscar Montiel, Oscar Castillo, Patricia Melin, Roberto Sepulveda
    Pages 195-212
  7. C. A. Coello Coello, G. Toscano Pulido, E. Mezura Montes
    Pages 213-231
  8. J. Kubalík, L. Rothkrantz, J. Lažanský
    Pages 233-253
  9. F. Bellas, J. A. Becerra, R. J. Duro
    Pages 255-267
  10. J.-Q. Liu, K. Shimohara
    Pages 269-284

About this book

Introduction

The last decade of the 20th century has witnessed a surge of interest in num- ical, computation-intensive approaches to information processing. The lines that draw the boundaries among statistics, optimization, arti cial intelligence and information processing are disappearing, and it is not uncommon to nd well-founded and sophisticated mathematical approaches in application - mains traditionally associated with ad-hoc programming. Heuristics has - come a branch of optimization and statistics. Clustering is applied to analyze soft data and to provide fast indexing in the World Wide Web. Non-trivial matrix algebra is at the heart of the last advances in computer vision. The breakthrough impulse was, apparently, due to the rise of the interest in arti cial neural networks, after its rediscovery in the late 1980s. Disguised as ANN, numerical and statistical methods made an appearance in the - formation processing scene, and others followed. A key component in many intelligent computational processing is the search for an optimal value of some function. Sometimes, this function is not evident and it must be made explicit in order to formulate the problem as an optimization problem. The search - ten takes place in high-dimensional spaces that can be either discrete, or c- tinuous or mixed. The shape of the high-dimensional surface that corresponds to the optimized function is usually very complex. Evolutionary algorithms are increasingly being applied to information processing applications that require any kind of optimization.

Keywords

3D Computer Graphi Evolutionary Algorithms Hardware Design Image Processing Information Processing Process Control Robotics Triangulation algorithms artificial intelligence evolutionary algorithm genetic programming learning natural language

Editors and affiliations

  • Xindong Wu
  • Lakhmi Jain
  • Manuel Graña
    • 1
  • Richard J. Duro
    • 2
  • Alicia d’Anjou
    • 1
  • Paul P. Wang
    • 3
  1. 1.Dept. CCIAUniversidad Pais VascoSpain
  2. 2.Grupo de Sistemas AutónomosUniversidade da CoruñaSpain
  3. 3.Duke UniversityDurhamUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b138854
  • Copyright Information Springer-Verlag London Limited 2005
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-85233-866-4
  • Online ISBN 978-1-84628-117-4
  • About this book