Computational Intelligence

1. Communications for CI Systems

   1. Computational Intelligence for the Collaborative Identification of Distributed Systems

      • Giorgio Biagetti, Paolo Crippa, Francesco Gianfelici, Claudio Turchetti

      Pages 475-500

   2. Collaboration at the Basis of Sharing Focused Information: The Opportunistic Networks

      • Bruno Apolloni, Guglielmo Apolloni, Simone Bassis, Gian Luca Galliani, Gianpaolo Rossi

      Pages 501-524

2. Artificial Immune Systems

   1. Front Matter

      Pages 525-525

      PDF

   2. Exploiting Collaborations in the Immune System: The Future of Artificial Immune Systems

      • Emma Hart, Chris McEwan, Despina Davoudani

      Pages 527-558

3. Parallel Evolutionary Algorithms

   1. Front Matter

      Pages 559-559

      PDF

   2. Evolutionary Computation: Centralized, Parallel or Collaborative

      • Heinz Mühlenbein

      Pages 561-595

4. CI for Clustering and Classification

   1. Front Matter

      Pages 597-597

      PDF

   2. Fuzzy Clustering of Likelihood Curves for Finding Interesting Patterns in Expression Profiles

      • Claudia Hundertmark, Lothar Jänsch, Frank Klawonn

      Pages 599-622

   3. A Hybrid Rule-Induction/Likelihood-Ratio Based Approach for Predicting Protein-Protein Interactions

      • Mudassar Iqbal, Alex A. Freitas, Colin G. Johnson

      Pages 623-637

   4. Improvements in Flock-Based Collaborative Clustering Algorithms

      • Esin Saka, Olfa Nasraoui

      Pages 639-672

   5. Combining Statistics and Case-Based Reasoning for Medical Research

      • Rainer Schmidt, Olga Vorobieva

      Pages 673-696

   6. Collaborative and Experience-Consistent Schemes of System Modelling in Computational Intelligence

      • Witold Pedrycz

      Pages 697-723

5. Back Matter

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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.

• Multi-agent system
• agents
• case-based reasoning
• computational intelligence
• evolution
• evolutionary algorithm
• evolutionary computation
• fuzzy logic
• fuzzy system
• genetic programming
• learning
• modeling

• 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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