Fuzzy Evolutionary Computation

1. Front Matter

   Pages i-xv

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2. Fundamentals

   1. Front Matter

      Pages 1-1

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   2. Evolutionary Algorithms

      • Zbigniew Michalewicz, Robert Hinterding, Maciej Michalewicz

      Pages 3-31

   3. On the Combination of Fuzzy Logic and Evolutionary Computation: A Short Review and Bibliography

      • O. Cordon, F. Herrera, M. Lozano

      Pages 33-56

   4. Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components

      • Michael A. Lee, Henrik Esbensen

      Pages 57-78

3. Methodology and Algorithms

   1. Front Matter

      Pages 79-79

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   2. GA Algorithms in Intelligent Robots

      • Toshio Fukuda, Naoyuki Kubota, Takemasa Arakawa

      Pages 81-105

   3. Development of If-Then Rules with the Use of DNA Coding

      • Takeshi Furuhashi

      Pages 107-125

   4. Genetic-Algorithm-Based Approaches to Classification Problems

      • Hisao Ishibuchi, Tadahiko Murata, Tomoharu Nakashima

      Pages 127-153

   5. Multiobjective Fuzzy Satisficing Methods for 0–1 Knapsack Problems through Genetic Algorithms

      • Masatoshi Sakawa, Toshihiro Shibano

      Pages 155-177

   6. Multistage Evolutionary Optimization of Fuzzy Systems - Application to Optimal Fuzzy Control

      • Janusz Kacprzyk

      Pages 179-198

   7. Evolutionary Learning in Neural Fuzzy Control Systems

      • Derek A. Linkens, H. Okola Nyongesa

      Pages 199-222

   8. Stable Identification and Adaptive Control - A Dynamic Fuzzy Logic System Approach

      • George Vukovich, James X. Lee

      Pages 223-248

   9. Evolutionary Based Learning of Fuzzy Controllers

      • Luis Magdalena, Juan R. Velasco

      Pages 249-268

   10. GA-Based Generation of Fuzzy Rules

       • Oliver Nelles

       Pages 269-295

4. Bibliography

   1. Front Matter

      Pages 297-297

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   2. An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic

      • Jarmo T. Alander

      Pages 299-318

5. Back Matter

   Pages 319-320

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As of today, Evolutionary Computing and Fuzzy Set Computing are two mature, wen -developed, and higbly advanced technologies of information processing. Bach of them has its own clearly defined research agenda, specific goals to be achieved, and a wen setUed algorithmic environment. Concisely speaking, Evolutionary Computing (EC) is aimed at a coherent population -oriented methodology of structural and parametric optimization of a diversity of systems. In addition to this broad spectrum of such optimization applications, this paradigm otTers an important ability to cope with realistic goals and design objectives reflected in the form of relevant fitness functions. The GA search (which is often regarded as a dominant domain among other techniques of EC such as evolutionary strategies, genetic programming or evolutionary programming) delivers a great deal of efficiency helping navigate through large search spaces. The main thrust of fuzzy sets is in representing and managing nonnumeric (linguistic) information. The key notion (whose conceptual as weH as algorithmic importance has started to increase in the recent years) is that of information granularity. It somewhat concurs with the principle of incompatibility coined by L. A. Zadeh. Fuzzy sets form a vehic1e helpful in expressing a granular character of information to be captured. Once quantified via fuzzy sets or fuzzy relations, the domain knowledge could be used efficiently very often reducing a heavy computation burden when analyzing and optimizing complex systems.

• algorithms
• classification
• evolution
• evolutionary algorithm
• evolutionary computation
• fuzzy
• genetic algorithm
• genetic algorithms
• intelligent systems
• knowledge
• learning
• optimization
• robot
• sets

• The University of Manitoba, Winnipeg, Canada

  Witold Pedrycz

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