Parallel Problem Solving from Nature - PPSN IX

1. Front Matter

   Pages I-XIX

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2. Theory

   1. Evolutionary Optimization in Spatio–temporal Fitness Landscapes

      • Hendrik Richter

      Pages 1-10

   2. Cumulative Step Length Adaptation on Ridge Functions

      • Dirk V. Arnold

      Pages 11-20

   3. General Lower Bounds for Evolutionary Algorithms

      • Olivier Teytaud, Sylvain Gelly

      Pages 21-31

   4. On the Ultimate Convergence Rates for Isotropic Algorithms and the Best Choices Among Various Forms of Isotropy

      • Olivier Teytaud, Sylvain Gelly, Jérémie Mary

      Pages 32-41

   5. Mixed-Integer NK Landscapes

      • Rui Li, Michael T. M. Emmerich, Jeroen Eggermont, Ernst G. P. Bovenkamp, Thomas Bäck, Jouke Dijkstra et al.

      Pages 42-51

   6. How Comma Selection Helps with the Escape from Local Optima

      • Jens Jägersküpper, Tobias Storch

      Pages 52-61

   7. When Do Heavy-Tail Distributions Help?

      • Nikolaus Hansen, Fabian Gemperle, Anne Auger, Petros Koumoutsakos

      Pages 62-71

   8. Self-adaptation on the Ridge Function Class: First Results for the Sharp Ridge

      • Hans-Georg Beyer, Silja Meyer-Nieberg

      Pages 72-81

   9. Searching for Balance: Understanding Self-adaptation on Ridge Functions

      • Monte Lunacek, Darrell Whitley

      Pages 82-91

   10. Diversity Loss in General Estimation of Distribution Algorithms

       • Jonathan L. Shapiro

       Pages 92-101

   11. Information Perspective of Optimization

       • Yossi Borenstein, Riccardo Poli

       Pages 102-111

3. New Algorithms

   1. A Novel Negative Selection Algorithm with an Array of Partial Matching Lengths for Each Detector

      • Wenjian Luo, Xin Wang, Ying Tan, Xufa Wang

      Pages 112-121

   2. Hierarchical BOA, Cluster Exact Approximation, and Ising Spin Glasses

      • Martin Pelikan, Alexander K. Hartmann, Kumara Sastry

      Pages 122-131

   3. Towards an Adaptive Multimeme Algorithm for Parameter Optimisation Suiting the Engineers’ Needs

      • Wilfried Jakob

      Pages 132-141

   4. Niche Radius Adaptation in the CMA-ES Niching Algorithm

      • Ofer M. Shir, Thomas Bäck

      Pages 142-151

   5. A Tabu Search Evolutionary Algorithm for Solving Constraint Satisfaction Problems

      • B. G. W. Craenen, B. Paechter

      Pages 152-161

   6. cAS: Ant Colony Optimization with Cunning Ants

      • Shigeyoshi Tsutsui

      Pages 162-171

   7. Genetic Algorithm Based on Independent Component Analysis for Global Optimization

      • Gang Li, Kin Hong Lee, Kwong Sak Leung

      Pages 172-181

   8. Improved Squeaky Wheel Optimisation for Driver Scheduling

      • Uwe Aickelin, Edmund K. Burke, Jingpeng Li

      Pages 182-191

We are very pleased to present this LNCS volume, the proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN IX). PPSNisoneofthemostrespectedandhighlyregardedconferenceseriesinevo- tionary computation and natural computing / computation. This biennial event was ?rst held in Dortmund in 1990, and then in Brussels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), Granada (2002), and Birmingham(2004).PPSNcontinuestobetheconferenceofchoicebyresearchers all over the world, who value its high quality. We received 255 paper submissions this year. After an extensive peer review process involving more than 1000 reviews, the programme committee selected the top 106 papers for inclusion in this volume and, of course, for presentation at the conference. This represents an acceptance rate of 42%. 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 - pers at PPSN IX were presented as posters. There were 7 sessions: each session consisting of around 15 papers. For each session, we covered as wide a range of topics as possible so that participants with di?erent interests could ?nd some relevant papers in every session.

• Scheduling
• adaptive systems
• algorithmic learning
• classifier systems
• evolution algorithms
• evolution strategies
• evolutionary algorithm
• evolutionary computing
• genetic algorithms
• genetic programming
• modeling
• modularity
• object oriented design
• programming
• robot
• algorithm analysis and problem complexity

• Science Institute, University of Iceland, Iceland

  Thomas Philip Runarsson

• Vorarlberg University of Applied Sciences, Dornbirn, Austria

  Hans-Georg Beyer

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

  Edmund Burke

• Depto. Arquitectura y Tecnologa de Computadores, ETS Ingeiera Informtica, Granada, Spain

  Juan J. Merelo-Guervós

• Colorado State University, Fort Collins, USA

  L. Darrell Whitley

• University of Birmingham, Birmingham, UK

  Xin Yao

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