Parallel Problem Solving from Nature

1. Front Matter

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2. Introduction

   Pages 1-2

3. Global convergence of genetic algorithms: A markov chain analysis

   • A. E. Eiben, E. H. L. Aarts, K. M. Van Hee

   Pages 3-12

4. The theory of virtual alphabets

   • David E. Goldberg

   Pages 13-22

5. Towards an optimal mutation probability for genetic algorithms

   • Jürgen Hesser, Reinhard Männer

   Pages 23-32

6. An alternative Genetic Algorithm

   • Jürgen Hesser, Reinhard Männer

   Pages 33-37

7. An analysis of the interacting roles of population size and crossover in genetic algorithms

   • Kenneth A. De Jong, William M. Spears

   Pages 38-47

8. Gleam a system for simulated "intuitive learning"

   • Christian Blume

   Pages 48-54

9. Genetic algorithms and highly constrained problems: The time-table case

   • Alberto Colorni, Marco Dorigo, Vittorio Maniezzo

   Pages 55-59

10. An evolution standing on the design of redundant manipulators

    • Yuval Davidor, Yaron Goldberg

    Pages 60-69

11. Redundant coding of an NP-complete problem allows effective Genetic Algorithm search

    • Marleen Gerrits, Paulien Hogeweg

    Pages 70-74

12. Circuit partitioning with genetic algorithms using a coding scheme to preserve the structure of a circuit

    • Martin Hulin

    Pages 75-79

13. Genetic algorithms, production plan optimisation and scheduling

    • Philip Husbands, Frank Mill, Stephen Warrington

    Pages 80-84

14. System identification using genetic algorithms

    • Timothy Johnson, Philip Husbands

    Pages 85-89

15. Conformational analysis of DNA using genetic algorithms

    • C. B. Lucasius, S. Werten, A. H. J. M. van Aert, G. Kateman, M. J. J. Blommers

    Pages 90-97

16. Operator-oriented genetic algorithm and its application to sliding block puzzle problem

    • Jung-Yul Suh, Chan-Do Lee

    Pages 98-103

17. A topology exploiting genetic algorithm to control dynamic systems

    • Dirk Thierens, Leo Vercauteren

    Pages 104-108

18. Genetic local search algorithms for the traveling salesman problem

    • Nico L. J. Ulder, Emile H. L. Aarts, Hans-Jürgen Bandelt, Peter J. M. van Laarhoven, Erwin Pesch

    Pages 109-116

19. Genetic programming artificial nervous systems artificial embryos and embryological electronics

    • Hugo de Garis

    Pages 117-123

20. Concept formation and decision tree induction using the genetic programming paradigm

    • John R. Koza

    Pages 124-128

With the appearance of massively parallel computers, increased attention has been paid to algorithms which rely upon analogies to natural processes. This development defines the scope of the PPSN conference at Dortmund in 1990 whose proceedings are presented in this volume. The subjects treated include: - Darwinian methods such as evolution strategies and genetic algorithms; - Boltzmann methods such as simulated annealing; - Classifier systems and neural networks; - Transfer of natural metaphors to artificial problem solving. The main objectives of the conference were: - To gather theoretical results about and experimental comparisons between these algorithms, - To discuss various implementations on different parallel computer architectures, - To summarize the state of the art in the field, which was previously scattered widely both among disciplines and geographically.

• Evolution Strategies
• Evolutionsstrategien
• Genetic Algorithms
• Neural Networks
• Parallel Processing
• Problem Solving
• Problemlösen (Math.)
• Problemlösungs-Strategien
• Simulated Annealing
• algorithms
• genetic algorithm
• neural network
• algorithm analysis and problem complexity

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