Parallel Problem Solving from Nature - PPSN VIII

1. Front Matter

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2. Theory

   1. On the Quality Gain of (1,λ)-ES Under Fitness Noise

      • Hans-Georg Beyer, Silja Meyer-Nieberg

      Pages 1-10

   2. Fitness Distributions and GA Hardness

      • Yossi Borenstein, Riccardo Poli

      Pages 11-20

   3. Experimental Supplements to the Theoretical Analysis of EAs on Problems from Combinatorial Optimization

      • Patrick Briest, Dimo Brockhoff, Bastian Degener, Matthias Englert, Christian Gunia, Oliver Heering et al.

      Pages 21-30

   4. The Ising Model: Simple Evolutionary Algorithms as Adaptation Schemes

      • Patrick Briest, Dimo Brockhoff, Bastian Degener, Matthias Englert, Christian Gunia, Oliver Heering et al.

      Pages 31-40

   5. Evolutionary Algorithms with On-the-Fly Population Size Adjustment

      • A. E. Eiben, Elena Marchiori, V. A. Valkó

      Pages 41-50

   6. Search Space Features Underlying the Performance of Stochastic Local Search Algorithms for MAX-SAT

      • Holger H. Hoos, Kevin Smyth, Thomas Stützle

      Pages 51-60

   7. Bridging the Gap Between Theory and Practice

      • Thomas Jansen, R. Paul Wiegand

      Pages 61-71

   8. A Reduced Markov Model of GAs Without the Exact Transition Matrix

      • Cheah C. J. Moey, Jonathan E. Rowe

      Pages 72-80

   9. Expected Runtimes of a Simple Evolutionary Algorithm for the Multi-objective Minimum Spanning Tree Problem

      • Frank Neumann

      Pages 81-90

   10. On the Importance of Information Speed in Structured Populations

       • Mike Preuss, Christian Lasarczyk

       Pages 91-100

   11. Estimating the Number of Solutions for SAT Problems

       • Colin R. Reeves, Mériéma Aupetit-Bélaidouni

       Pages 101-110

   12. Behavior of Evolutionary Algorithms in Chaotically Changing Fitness Landscapes

       • Hendrik Richter

       Pages 111-120

   13. Expected Rates of Building Block Discovery, Retention and Combination Under 1-Point and Uniform Crossover

       • Cameron Skinner, Patricia Riddle

       Pages 121-130

   14. An Analysis of the Effectiveness of Multi-parent Crossover

       • Chuan-Kang Ting

       Pages 131-140

   15. On the Use of a Non-redundant Encoding for Learning Bayesian Networks from Data with a GA

       • Steven van Dijk, Dirk Thierens

       Pages 141-150

   16. Phase Transition Properties of Clustered Travelling Salesman Problem Instances Generated with Evolutionary Computation

       • Jano I. van Hemert, Neil B. Urquhart

       Pages 151-160

   17. A Simple Two-Module Problem to Exemplify Building-Block Assembly Under Crossover

       • Richard A. Watson

       Pages 161-171

   18. Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms

       • Bo Yuan, Marcus Gallagher

       Pages 172-181

3. New Algorithms

   1. LS-CMA-ES: A Second-Order Algorithm for Covariance Matrix Adaptation

      • Anne Auger, Marc Schoenauer, Nicolas Vanhaecke

      Pages 182-191

We are very pleased to present this LNCS volume, the proceedings of the 8th InternationalConferenceonParallelProblemSolvingfromNature(PPSNVIII). PPSN is one of the most respected and highly regarded conference series in evolutionary computation and natural computing/computation. This biennial eventwas?rstheldinDortmundin1990,andtheninBrussels(1992),Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), and Granada (2002). PPSN VIII continues to be the conference of choice by researchers all over the world who value its high quality. We received a record 358 paper submissions this year. After an extensive peer review process involving more than 1100 reviews, the programme c- mittee selected the top 119 papers for inclusion in this volume and, of course, for presentation at the conference. This represents an acceptance rate of 33%. Please note that review reports with scores only but no textual comments were not considered in the chairs’ ranking decisions. The papers included in this volume cover a wide range of topics, from e- lutionary computation to swarm intelligence and from bio-inspired computing to real-world applications. They represent some of the latest and best research in evolutionary and natural computation. Following the PPSN tradition, all - persatPPSNVIII werepresentedasposters.Therewere7 sessions:eachsession consisting of around 17 papers. For each session, we covered as wide a range of topics as possible so that participants with di?erent interests would ?nd some relevant papers at every session.

• Scala
• Scheduling
• Scheme
• adaptive systems
• algorithmic learning
• biocomputing
• biologically inspired computing
• classifier systems
• evolutionary computing
• evolutionary rob
• genetic algorithms
• genetic programming
• multi-objective optimization
• natural computing
• neural computing
• algorithm analysis and problem complexity

• University of Birmingham, Birmingham, UK

  Xin Yao

• Automated Scheduling, Optimisation and Planning Group, School of Computer Science & IT, University of Nottingham, Nottingham, UK

  Edmund K. Burke

• Intelligent Systems Group Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastian, Donostia, Spain

  José A. Lozano

• University of the West of England, Bristol, United Kingdom

  Jim Smith

• GeNeura Team, Depto. Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain

  Juan Julián Merelo-Guervós

• School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, UK

  John A. Bullinaria

• School of Computer Science, University of Birmingham, Birmingham, Great Britain

  Jonathan E. Rowe

• School of Computer Science, University of Birmingham, United Kingdom

  Peter Tiňo

• School of Computer Science, The University of Birmingham, Birmingham, UK

  Ata Kabán

• Faculty of Computer Science, Dortmund University of Technology, Dortmund, Germany

  Hans-Paul Schwefel

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