Computational Intelligence Collaboration, Fusion and Emergence /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Mumford, Christine L. (Επιμελητής έκδοσης), Jain, Lakhmi C. (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
• Σειρά: Intelligent Systems Reference Library, 1
• Θέματα: Computer science.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Computer Science.; Artificial Intelligence (incl. Robotics).; Appl.Mathematics/Computational Methods of Engineering.; Applications of Mathematics.

 This book is the first in a new series entitled "Intelligent Systems Reference Library". It is a collection of chapters written by leading experts, covering a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence (CI). Authors in this collection 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. Fusion 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. The book covers a wide range of cutting edge techniques and applications, and is divided into the following parts: I. Introduction II. Fusing evolutionary algorithms and fuzzy logic III. Adaptive solution schemes IV. Multi-agent systems V. Computer vision VI. Communication for CI systems VII. Artificial immune systems VIII. Parallel evolutionary algorithms IX. CI for clustering and classification. 

 Synergy in Computational Intelligence -- Computational Intelligence: The Legacy of Alan Turing and John von Neumann -- 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 -- Adaptive Solution Schemes -- Exploring Hyper-heuristic Methodologies with Genetic Programming -- Adaptive Constraint Satisfaction: The Quickest First Principle -- 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 -- Computer Vision -- Predicting Trait Impressions of Faces Using Classifier Ensembles -- The Analysis of Crowd Dynamics: From Observations to Modelling -- Communications for CI Systems -- Computational Intelligence for the Collaborative Identification of Distributed Systems -- Collaboration at the Basis of Sharing Focused Information: The Opportunistic Networks -- Artificial Immune Systems -- Exploiting Collaborations in the Immune System: The Future of Artificial Immune Systems -- Parallel Evolutionary Algorithms -- Evolutionary Computation: Centralized, Parallel or Collaborative -- CI for Clustering and Classification -- Fuzzy Clustering of Likelihood Curves for Finding Interesting Patterns in Expression Profiles -- A Hybrid Rule-Induction/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.