Stochastic Algorithms: Foundations and Applications

1. Front Matter

   Pages I-VIII

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2. Randomized Communication Protocols

   • Juraj Hromkovič

   Pages 1-32

3. Optimal Mutation Rate Using Bayesian Priors for Estimation of Distribution Algorithms

   • Thilo Mahnig, Heinz Mühlenbein

   Pages 33-48

4. An Experimental Assessment of a Stochastic, Anytime, Decentralized, Soft Colourer for Sparse Graphs

   • Stephen Fitzpatrick, Lambert Meertens

   Pages 49-64

5. Randomized Branching Programs

   • Martin Sauerho.

   Pages 65-72

6. Yet Another Local Search Method for Constraint Solving

   • Philippe Codognet, Daniel Diaz

   Pages 73-90

7. An Evolutionary Algorithm for the Sequence Coordination in Furniture Production

   • Carlo Meloni

   Pages 91-106

8. Evolutionary Search for Smooth Maps in Motor Control Unit Calibration

   • Jan Poland, Kosmas Knödler, Alexander Mitterer, Thomas Fleischhauer, Frank Zuber-Goos, Andreas Zell

   Pages 107-116

9. Some Notes on Random Satisfiability

   • Gregory B. Sorkin

   Pages 117-130

10. Prospects for Simulated Annealing Algorithms in Automatic Differentiation

    • Uwe Naumann, Peter Gottschling

    Pages 131-144

11. Optimization and Simulation: Sequential Packing of Flexible Objects Using Evolutionary Algorithms

    • Henning Behnke, Michael Kolonko, Ulrich Mertins, Stefan Schnitter

    Pages 145-154

12. Stochastic Finite Learning

    • Thomas Zeugmann

    Pages 155-172

13. Sequential Sampling Algorithms: Unified Analysis and Lower Bounds

    • Ricard Gavaldà, Osamu Watanabe

    Pages 173-188

14. Approximate Location of Relevant Variables under the Crossover Distribution

    • Peter Damaschke

    Pages 189-202

15. Back Matter

    Pages 203-203

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SAGA 2001, the ?rst Symposium on Stochastic Algorithms, Foundations and Applications, took place on December 13–14, 2001 in Berlin, Germany. The present volume comprises contributed papers and four invited talks that were included in the ?nal program of the symposium. Stochastic algorithms constitute a general approach to ?nding approximate solutions to a wide variety of problems. Although there is no formal proof that stochastic algorithms perform better than deterministic ones, there is evidence by empirical observations that stochastic algorithms produce for a broad range of applications near-optimal solutions in a reasonable run-time. The symposium aims to provide a forum for presentation of original research in the design and analysis, experimental evaluation, and real-world application of stochastic algorithms. It focuses, in particular, on new algorithmic ideas invo- ing stochastic decisions and exploiting probabilistic properties of the underlying problem domain. The program of the symposium re?ects the e?ort to promote cooperation among practitioners and theoreticians and among algorithmic and complexity researchers of the ?eld. In this context, we would like to express our special gratitude to DaimlerChrysler AG for supporting SAGA 2001. The contributed papers included in the proceedings present results in the following areas: Network and distributed algorithms; local search methods for combinatorial optimization with application to constraint satisfaction problems, manufacturing systems, motor control unit calibration, and packing ?exible - jects; and computational learning theory.

• Erfüllbarkeitsproblem der Aussagenlogik
• Stochastic algorithms
• Variable
• algorithms
• approximation
• calculus
• complexity
• constraint satisfaction
• evolutionary algorithm
• nondeterministic computation
• optimization
• randomization
• randomized communication
• randomized computation
• stochastic learning
• algorithm analysis and problem complexity
• combinatorics

• Institute for Computer Architecture and Software Engineering, GMD - National ResearchCenter for Information Technology, Berlin-Adlershof, Germany

  Kathleen Steinhöfel

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