Evolutionary Algorithms

1. Front Matter

   Pages i-x

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2. Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization

   • Ioannis T. Christou, Wayne Martin, Robert R. Meyer

   Pages 1-15

3. Telecommunication Network Optimization with Genetic Algorithms: A Decade of Practice

   • Lawrence Davis

   Pages 17-32

4. Using Evolutionary Algorithms to Search for Control Parameters in a Nonlinear Partial Differential Equation

   • Rogene M. Eichler West, Erik De Schutter, George L. Wilcox

   Pages 33-64

5. Applying Genetic Algorithms to Real-World Problems

   • Emanuel Falkenauer

   Pages 65-88

6. An Overview of Evolutionary Programming

   • David B. Fogel

   Pages 89-109

7. A Hierarchical Genetic Algorithm for System Identification and Curve Fitting with a Supercomputer Implementation

   • Mehmet Gulsen, Alice E. Smith

   Pages 111-137

8. Experiences with the PGAPack Parallel Genetic Algorithm Library

   • David Levine, Philip Hallstrom, David Noelle, Brian Walenz

   Pages 139-149

9. The Significance of the Evaluation Function in Evolutionary Algorithms

   • Zbigniew Michalewicz

   Pages 151-166

10. Genetic Algorithm Optimization of Atomic Clusters

    • J. R. Morris, D. M. Deaven, K. M. Ho, C. Z. Wang, B. C. Pan, J. G. Wacker et al.

    Pages 167-175

11. Search, Binary Representations and Counting Optima

    • Soraya Rana, L. Darrell Whitley

    Pages 177-189

12. An Investigation of GA Performance Results for Different Cardinality Alphabets

    • Jackie Rees, Gary J. Koehler

    Pages 191-206

13. Genetic Algorithms and the Design of Experiments

    • Colin R. Reeves, Christine C. Wright

    Pages 207-226

14. Efficient Parameter Optimization Based on Combination of Direct Global and Local Search Methods

    • Michael Syrjakow, Helena Szczerbicka

    Pages 227-249

15. What are Genetic Algorithms? A Mathematical Prespective

    • Michael D. Vose

    Pages 251-276

16. Survey of Projects Involving Evolutionary Algorithms Sponsored by the Electric Power Research Institute

    • A. Martin Wildberger

    Pages 277-293

17. Back Matter

    Pages 294-300

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This IMA Volume in Mathematics and its Applications EVOLUTIONARY ALGORITHMS is based on the proceedings of a workshop that was an integral part of the 1996-97 IMA program on "MATHEMATICS IN HIGH-PERFORMANCE COMPUTING." I thank Lawrence David Davis (Tica Associates), Kenneth De Jong (Computer Science, George Mason University), Michael D. Vose (Computer Science, The University of Tennessee), and L. Darrell Whitley (Computer Science, Colorado State University) for their excellent work in organizing the workshop and for editing the proceedings. Further appreciation is ex­ tended to Donald G. Truhlar (Chemistry and Supercomputing Institute, University of Minnesota) who was also one of the workshop organizers. In addition, I also take this opportunity to thank the National Science Foundation (NSF), Minnesota Supercomputing Institute (MSI), and the Army Research Office (ARO), whose financial support made the workshop possible. Willard Miller, Jr., Professor and Director v PREFACE The IMA Workshop on Evolutionary Algorithms brought together many of the top researchers working in the area of Evolutionary Com­ putation for a week of intensive interaction. The field of Evolutionary Computation has developed significantly over the past 30 years and today consists a variety of subfields such as genetic algorithms, evolution strate­ gies, evolutionary programming, and genetic programming, each with their own algorithmic perspectives and goals.

• control
• evolution
• evolutionary algorithm
• evolutionary computation
• evolutionary strategies
• genetic algorithms
• genetic programming
• optimization

• Tica Associates, Newbury, USA

  Lawrence David Davis

• Computer Science Department, George Mason University, Fairfax, USA

  Kenneth Jong

• Computer Science Department, The University of Tennessee, Knoxville, USA

  Michael D. Vose

• Computer Science Department, Colorado State University, Fort Collins, USA

  L. Darrell Whitley

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