Evolutionary Algorithms in Engineering Applications

1. Front Matter

   Pages I-XXI

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2. Introduction

   1. Front Matter

      Pages 1-1

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   2. Evolutionary Algorithms — An Overview

      • Dipankar Dasgupta, Zbigniew Michalewicz

      Pages 3-28

   3. Robust Encodings in Genetic Algorithms

      • Simon Ronald

      Pages 29-44

3. Architecture and Civil Engineering

   1. Front Matter

      Pages 45-45

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   2. Genetic Engineering and Design Problems

      • John S. Gero, Vladimir A. Kazakov, Thorsten Schnier

      Pages 47-68

   3. The Generation of Form Using an Evolutionary Approach

      • M. A. Rosenman

      Pages 69-85

   4. Evolutionary Optimization of Composite Structures

      • Rodolphe Le Riche, Raphael T. Haftka

      Pages 87-102

   5. Flaw Detection and Configuration with Genetic Algorithms

      • Sushil J. Louis, Fang Zhao, Xiaogang Zeng

      Pages 103-116

   6. A Genetic Algorithm Approach for River Management

      • J. P. King, H. S. Fahmy, M. W. Wentzel

      Pages 117-133

   7. Hazards in Genetic Design Methodologies

      • Gerald P. Roston

      Pages 135-152

4. Computer Science and Engineering

   1. Front Matter

      Pages 153-153

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   2. The Identification and Characterization of Workload Classes

      • Chrisila C. Pettey, Patricia White, Larry Dowdy, Darrell Burkhead

      Pages 155-172

   3. Lossless and Lossy Data Compression

      • Wee Keong Ng, Sunghyun Choi, Chinya Ravishankar

      Pages 173-188

   4. Database Design with Genetic Algorithms

      • Walter Cedeño, V. Rao Vemuri

      Pages 189-206

   5. Designing Multiprocessor Scheduling Algorithms Using a Distributed Genetic Algorithm System

      • Kelvin K. Yue, David J. Lilja

      Pages 207-222

   6. Prototype Based Supervised Concept Learning Using Genetic Algorithms

      • Sandip Sen, Leslie Knight, Kevin Legg

      Pages 223-239

   7. Prototyping Intelligent Vehicle Modules Using Evolutionary Algorithms

      • Shumeet Baluja, Rahul Sukthankar, John Hancock

      Pages 241-257

   8. Gate-Level Evolvable Hardware: Empirical Study and Application

      • Hitoshi Iba, Masaya Iwata, Tetsuya Higuchi

      Pages 259-276

   9. Physical Design of VLSI Circuits and the Application of Genetic Algorithms

      • Jens Lienig

      Pages 277-292

Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.

• Datenverarbeitung
• algorithm
• algorithms
• architecture
• computer
• computer science
• data compression
• electronics
• evolutionary algorithm
• genetic algorithms
• linear optimization
• mechanism
• model
• optimization
• programming
• complexity

• Mathematical Sciences Department, University of Memphis, Memphis, USA

  Dipankar Dasgupta

• Department of Computer Science, University of North Carolina, Charlotte, USA

  Zbigniew Michalewicz

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