Springer, 2009. - 726 p.
This book covers a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence (CI). Authors recognize the limitations of individual paradigms, and propose some practical and novel ways in which different CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful problem-solving environments. Common themes to be found in the various chapters of this collection include the following: Fusion, Collaboration, and Emergence.
This book describes the hybridization of two or more techniques, at least one of which will involve CI. Collaboration ensures that the different techniques work effectively together. Finally, Emergence refers to the phenomenon that complex behaviour can arise as a result of collaboration between simple processing elements.
Contents Editors
Preface
Acknowledgments
The chapters and themes of the book
Part I Introduction
Synergy in Computational Intelligence
Computational Intelligence: The Legacy of Alan Turing and John von Neumann
Part II Fusing Evolutionary Algorithms and Fuzzy Logic
Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem
Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems
Multiobjective Genetic Fuzzy Systems
Part III Adaptive Solution Schemes
Exploring Hyper-heuristic Methodologies with Genetic Programming
Adaptive Constraint Satisfaction: The Quickest First Principle
Part IV Multi-Agent Systems
Collaborative Computational Intelligence in Economics
IMMUNE: A Collaborating Environment for Complex System Design
Bayesian Learning for Cooperation in Multi-Agent Systems
Collaborative Agents for Complex Problems Solving
Part V Computer Vision
Predicting Trait Impressions of Faces Using Classifier Ensembles
The Analysis of Crowd Dynamics: From Observations to Modelling
Part VI Communications for Cl Systems
Computational Intelligence for the Collaborative Identification of Distributed Systems
Collaboration at the Basis of Sharing Focused Information: The Opportunistic Networks
Part VII Artificial Immune Systems
Exploiting Collaborations in the Immune System: The Future of Artificial Immune Systems
Part VIII Parallel Evolutionary Algorithms
Evolutionary Computation: Centralized. Parallel or Collaborative
Part IX Cl for Clustering and Classification
Fuzzy Clustering of Likelihood Curves for Finding Interesting Patterns in Expression Profiles
A Hybrid Rule-lnduction/Likelihood-Ratio Based Approach for Predicting Protein-Protein Interactions
Improvements in Flock-Based Collaborative Clustering Algorithms
Combining Statistics and Case-Based Reasoning for Medical Research
Collaborative and Experience-Consistent Schemes of System Modelling in Computational Intelligence