Parallel Problem Solving from Nature - PPSN V

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

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.

• Evolutionary Programming
• Evolvable Systems
• Fuzzy Systems
• Genetic Algorithms
• Immune Systems
• Partition
• Scheduling
• neural network
• algorithm analysis and problem complexity

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