Engineering evolutionary intelligent systems
- Responsibility
- Ajith Abraham, Crina Grosan, Witold Pedrycz (eds.).
- Language
- English. English.
- Digital
- text file
- Imprint
- Berlin : Springer, ©2008.
- Physical description
- 1 online resource (xix, 444 pages) : illustrations
- Series
- Studies in computational intelligence ; v. 82.
Online
Available online
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Description
Creators/Contributors
Contents/Summary
- Bibliography
- Includes bibliographical references and index.
- Contents
-
- Front Matter; Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews; Genetically Optimized Hybrid Fuzzy Neural Networks: Analysis and Design of Rule-based Multi-layer Perceptron Architectures; Genetically Optimized Self-organizing Neural Networks Based on Polynomial and Fuzzy Polynomial Neurons: Analysis and Design; Evolution of Inductive Self-organizing Networks; Recursive Pattern based Hybrid Supervised Training; Enhancing Recursive Supervised Learning Using Clustering and Combinatorial Optimization (RSL-CC).
- Publisher's summary
-
Evolutionary design of intelligent systems is gaining much popularity due to its capabilities in handling several real world problems involving optimization, complexity, noisy and non-stationary environment, imprecision, uncertainty and vagueness. This edited volume "Engineering Evolutionary Intelligent Systems" deals with the theoretical and methodological aspects, as well as various evolutionary algorithm applications to many real world problems originating from science, technology, business or commerce. This volume comprises of 15 chapters including an introductory chapter which covers the fundamental definitions and outlines some important research challenges. The chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.
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Subjects
Bibliographic information
- Publication date
- 2008
- Series
- Studies in computational intelligence ; v. 82
- ISBN
- 9783540753964
- 3540753966
- 9783540753957 (cased)
- 3540753958 (cased)
- DOI
- 10.1007/978-3-540-75396-4
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