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Parallel Problem Solving from Nature - PPSN V

5th International Conference, Amsterdam, The Netherlands, September 27-30, 1998, Proceedings

  • Conference proceedings
  • © 1998

Overview

Editors:
  1. Agoston E. Eiben
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  2. Thomas Bäck
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  3. Marc Schoenauer
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  4. Hans-Paul Schwefel
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1498)

Included in the following conference series:

Conference proceedings info: PPSN 1998.

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About this book

This book constitutes the refereed proceedings of the 5th International Conference on Parallel Problem Solving from Nature, PPSN V, held in Amsterdam, The Netherlands, in September 1998.
The 101 papers included in their revised form were carefully reviewed and selected from a total of 185 submissions. The book is divided into topical sections on convergence theory; fitness landscape and problem difficulty; noisy and non-stationary objective functions; multi-criteria and constrained optimization; representative issues; selection, operators, and evolution schemes; coevolution and learning; cellular automata, fuzzy systems, and neural networks; ant colonies, immune systems, and other paradigms; TSP, graphs, and satisfiability; scheduling, partitioning, and packing; design and telecommunications; and model estimations and layout problems.

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Table of contents (101 papers)

  1. Front Matter

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  2. Modelling genetic algorithms: From Markov chains to dependence with complete connections

    • Alexandru Agapie
    Pages 3-12

  3. On the optimization of unimodal functions with the (1+1) evolutionary algorithm

    • Stefan Droste, Thomas Jansen, Ingo Wegener
    Pages 13-22

  4. A timing analysis of convergence to fitness sharing equilibrium

    • Jeffrey Horn, David E. Goldberg
    Pages 23-33

  5. Where elitists start limping evolution strategies at ridge functions

    • Ahmet Irfan Oyman, Hans-Georg Beyer, Hans-Paul Schwefel
    Pages 34-43

  6. A bit-wise epistasis measure for binary search spaces

    • Cyril Fonlupt, Denis Robilliard, Philippe Preux
    Pages 47-56

  7. Inside GA dynamics: Ground basis for comparison

    • Leila Kallel
    Pages 57-66

  8. The effect of spin-flip symmetry on the performance of the simple GA

    • Bart Naudts, Jan Naudts
    Pages 67-76

  9. Fitness distance correlation and Ridge functions

    • R. J. Quick, V. J. Rayward-Smith, G. D. Smith
    Pages 77-86

  10. Accelerating the convergence of evolutionary algorithms by fitness landscape approximation

    • Alain Ratle
    Pages 87-96

  11. Modeling building-block interdependency

    • Richard A. Watson, Gregory S. Hornby, Jordan B. Pollack
    Pages 97-106

  12. Mutate large, but inherit small! On the analysis of rescaled mutations in (\(\tilde 1,\tilde \lambda\))-ES with noisy fitness data

    • Hans-Georg Beyer
    Pages 109-118

  13. Creating robust solutions by means of evolutionary algorithms

    • Jürgen Branke
    Pages 119-128

  14. Analytic curve detection from a noisy binary edge map using genetic algorithm

    • Samarjit Chakraborty, Kalyanmoy Deb
    Pages 129-138

  15. A comparison of dominance mechanisms and simple mutation on non-stationary problems

    • Jonathan Lewis, Emma Hart, Graeme Ritchie
    Pages 139-148

  16. Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm

    • Naoki Mori, Hajime Kita, Yoshikazu Nishikawa
    Pages 149-158

  17. Optimization with noisy function evaluations

    • Volker Nissen, Jörn Propach
    Pages 159-168

  18. On risky methods for local selection under noise

    • Günter Rudolph
    Pages 169-177

  19. Polygenic inheritance — A haploid scheme that can outperform diploidy

    • Conor Ryan, J. J. Collins
    Pages 178-187

  20. Averaging efficiently in the presence of noise

    • Peter Stagge
    Pages 188-197
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