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Computational Intelligence

Collaboration, Fusion and Emergence

  • Book
  • © 2009

Overview

Editors:
  1. Christine L. Mumford

    1. School of Computer Science, Cardiff University , Cardiff, UK

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    You can also search for this editor in PubMed   Google Scholar

  2. Lakhmi C. Jain

    1. University of South Australia Adelaide , South Australia, Australia

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    You can also search for this editor in PubMed   Google Scholar

  • Latest research in the area of theory and applications of fusion of intelligent paradigms

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 1)

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Table of contents (22 chapters)

  1. Front Matter

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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Synergy in Computational Intelligence

      • Christine L. Mumford
      Pages 3-21

    3. Computational Intelligence: The Legacy of Alan Turing and John von Neumann

      • Heinz Mühlenbein
      Pages 23-43

  3. Fusing Evolutionary Algorithms and Fuzzy Logic

    1. Front Matter

      Pages 45-45
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    2. Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem

      • Mohammad A. Abido
      Pages 47-82

    3. Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems

      • F. Herrera, M. Lozano
      Pages 83-130

    4. Multiobjective Genetic Fuzzy Systems

      • Hisao Ishibuchi, Yusuke Nojima
      Pages 131-173

  4. Adaptive Solution Schemes

    1. Front Matter

      Pages 175-175
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    2. Exploring Hyper-heuristic Methodologies with Genetic Programming

      • Edmund K. Burke, Mathew R. Hyde, Graham Kendall, Gabriela Ochoa, Ender Ozcan, John R. Woodward
      Pages 177-201

    3. Adaptive Constraint Satisfaction: The Quickest First Principle

      • James E. Borrett, Edward P. K. Tsang
      Pages 203-230

  5. Multi-agent Systems

    1. Front Matter

      Pages 231-231
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    2. Collaborative Computational Intelligence in Economics

      • Shu-Heng Chen
      Pages 233-273

    3. IMMUNE: A Collaborating Environment for Complex System Design

      • Mahmoud Efatmaneshnik, Carl Reidsema
      Pages 275-320

    4. Bayesian Learning for Cooperation in Multi-Agent Systems

      • Mair Allen-Williams, Nicholas R. Jennings
      Pages 321-360

    5. Collaborative Agents for Complex Problems Solving

      • Minjie Zhang, Quan Bai, Fenghui Ren, John Fulcher
      Pages 361-399

  6. Computer Vision

    1. Front Matter

      Pages 401-401
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    2. Predicting Trait Impressions of Faces Using Classifier Ensembles

      • Sheryl Brahnam, Loris Nanni
      Pages 403-439

    3. The Analysis of Crowd Dynamics: From Observations to Modelling

      • B. Zhan, P. Remagnino, D. N. Monekosso, S. Velastin
      Pages 441-472

  7. Communications for CI Systems

    1. Front Matter

      Pages 473-473
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Keywords

About this book

This book is about synergy in computational intelligence (CI). It is a c- lection of chapters that covers a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence, but each one taking a somewhat pragmatic view. Many complex problems in the real world require the application of some form of what we loosely call “intel- gence”fortheirsolution. Fewcanbesolvedbythenaiveapplicationofasingle technique, however good it is. Authors in this collection recognize the li- tations of individual paradigms, and propose some practical and novel ways in which di?erent CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful probl- solving environments which exhibit synergy, i. e. , systems in which the whole 1 is greater than the sum of the parts . Computational intelligence is a relatively new term, and there is some d- agreement as to its precise de?nition. Some practitioners limit its scope to schemes involving evolutionary algorithms, neural networks, fuzzy logic, or hybrids of these. For others, the de?nition is a little more ?exible, and will include paradigms such as Bayesian belief networks, multi-agent systems, case-based reasoning and so on. Generally, the term has a similar meaning to the well-known phrase “Arti?cial Intelligence” (AI), although CI is p- ceived moreas a “bottom up” approachfrom which intelligent behaviour can emerge,whereasAItendstobestudiedfromthe“topdown”,andderivefrom pondering upon the “meaning of intelligence”. (These and other key issues will be discussed in more detail in Chapter 1.

Editors and Affiliations

  • School of Computer Science, Cardiff University , Cardiff, UK

    Christine L. Mumford

  • University of South Australia Adelaide , South Australia, Australia

    Lakhmi C. Jain

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