Overview

The main theme of this text is to highlight a synergistic effect that is emerging between fuzzy sets and evolutionary computation. This quantifies the main advantage arising from the symbiosis. The scope of the book is broad, ranging from coverage of fundamental ideas in fuzzy sets and evolutionary computation, through inclusion of cutting-edge research, to case studies. The focus is on the applied side of fuzzy evolutionary calculations. Each contribution is systematic and thorough in its presentations, and emphasizes design of evolutionary schemes that embraces various sources of domain knowledge. The authors have also included problem sets at the end of each chapter which explore specific conceptual and algorithmic points covered in the text.

Full Product Details

Author:   Witold Pedrycz
Publisher:   Springer
Imprint:   Springer
Edition:   1997 ed.
Dimensions:   Width: 15.50cm , Height: 2.00cm , Length: 23.50cm
Weight:   1.440kg
ISBN:  

9780792399421

ISBN 10:   0792399420
Pages:   320
Publication Date:   30 June 1997
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Editor's Preface. Part 1: Fundamentals. 1.1. Evolutionary Algorithms; Z. Michalewicz, et al. 1.2. On the Combination of Fuzzy Logic And Evolutionary Computation: A Short Review and Bibliography; O. Cordon, et al. 1.3. Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components; M.A. Lee, H. Esbensen. Part 2: Methodology and Algorithms. 2.1. GA Algorithms in Intelligent Robots; T. Fukuda, et al. 2.2. Development of If-Then Rules with the Use of DNA Coding; T. Furuhashi. 2.3. Genetic-Algorithm-Based Approaches to Classification Problems; H. Ishibuchi, et al. 2.4. Multiobjective Fuzzy Satisficing Methods for 0-1 Knapsack Problems Through Genetic Algorithms; M. Sakawa, T. Shibano. 2.5. Multistage Evolutionary Optimization of Fuzzy Systems - Application to Optimal Fuzzy Control; J. Kacprzyk. 2.6. Evolutionary Learning in Neural Fuzzy Control Systems; D.A. Linkens, H.O. Nyongesa. 2.7. Stable Identification and Adaptive Control - A Dynamic Fuzzy Logic System Approach; G. Vukovich, J.X. Lee. 2.8. Evolutionary Based Learning of Fuzzy Controllers; L. Magdalena, J.R. Velasco. 2.9. GA-Based Generation of Fuzzy Rules; O. Nelles. Part 3: Bibliography. 3.1. An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic; J.T. Alander. Subject Index.

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