11. PPSN 2010: Kraków, Poland

Theory of Evolutionary Computing (I)

• Süntje Böttcher, Benjamin Doerr, Frank Neumann:
  Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem. 1-10
  
• Dimo Brockhoff, Anne Auger, Nikolaus Hansen, Dirk V. Arnold, Tim Hohm:
  Mirrored Sampling and Sequential Selection for Evolution Strategies. 11-21
  
• David W. Corne, Alan P. Reynolds:
  Optimisation and Generalisation: Footprints in Instance Space. 22-31
  
• Benjamin Doerr, Leslie Ann Goldberg:
  Adaptive Drift Analysis. 32-41
  
• Benjamin Doerr, Thomas Jansen, Dirk Sudholt, Carola Winzen, Christine Zarges:
  Optimizing Monotone Functions Can Be Difficult. 42-51
  
• Mohamed Jebalia, Anne Auger:
  Log-Linear Convergence of the Scale-Invariant (µ/µ_w, lambda)-ES and Optimal µ for Intermediate Recombination for Large Population Sizes. 52-62
  
• Johannes W. Kruisselbrink, Michael Emmerich, André H. Deutz, Thomas Bäck:
  Exploiting Overlap When Searching for Robust Optima. 63-72
  
• Olaf Mersmann, Mike Preuss, Heike Trautmann:
  Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. 73-82
  
• Alberto Moraglio:
  One-Point Geometric Crossover. 83-93
  
• Rachael Morgan, Marcus Gallagher:
  When Does Dependency Modelling Help? Using a Randomized Landscape Generator to Compare Algorithms in Terms of Problem Structure. 94-103
  
• Gabriela Ochoa, Sébastien Vérel, Marco Tomassini:
  First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes. 104-113
  
• Karol Opara, Jaroslaw Arabas:
  Differential Mutation Based on Population Covariance Matrix. 114-123
  
• Dirk Sudholt:
  General Lower Bounds for the Running Time of Evolutionary Algorithms. 124-133
  
• Karsten Weicker:
  A Binary Encoding Supporting Both Mutation and Recombination. 134-143
  
• Yang Yu, Chao Qian, Zhi-Hua Zhou:
  Towards Analyzing Recombination Operators in Evolutionary Search. 144-153
  

Theory of Evolutionary Computing (II)

• Youhei Akimoto, Yuichi Nagata, Isao Ono, Shigenobu Kobayashi:
  Bidirectional Relation between CMA Evolution Strategies and Natural Evolution Strategies. 154-163
  
• James McDermott, Edgar Galván López, Michael O'Neill:
  A Fine-Grained View of GP Locality with Binary Decision Diagrams as Ant Phenotypes. 164-173
  
• Benjamin Doerr, Leslie Ann Goldberg:
  Drift Analysis with Tail Bounds. 174-183
  
• Benjamin Doerr, Daniel Johannsen, Timo Kötzing, Frank Neumann, Madeleine Theile:
  More Effective Crossover Operators for the All-Pairs Shortest Path Problem. 184-193
  
• Álvaro Fialho, Raymond Ros, Marc Schoenauer, Michèle Sebag:
  Comparison-Based Adaptive Strategy Selection with Bandits in Differential Evolution. 194-203
  
• Stefan Kratsch, Per Kristian Lehre, Frank Neumann, Pietro Simone Oliveto:
  Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation. 204-213
  
• Johannes W. Kruisselbrink, Michael Emmerich, Thomas Bäck:
  An Archive Maintenance Scheme for Finding Robust Solutions. 214-223
  
• Jörg Lässig, Dirk Sudholt:
  Experimental Supplements to the Theoretical Analysis of Migration in the Island Model. 224-233
  
• Jörg Lässig, Dirk Sudholt:
  General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms. 234-243
  
• Per Kristian Lehre:
  Negative Drift in Populations. 244-253
  
• Fabien Teytaud, Olivier Teytaud:
  Log(lambda) Modifications for Optimal Parallelism. 254-263
  
• Dirk Thierens:
  The Linkage Tree Genetic Algorithm. 264-273
  
• Renato Tinós, Shengxiang Yang:
  An Analysis of the XOR Dynamic Problem Generator Based on the Dynamical System. 274-283
  
• James M. Whitacre, Philipp Rohlfshagen, Axel Bender, Xin Yao:
  The Role of Degenerate Robustness in the Evolvability of Multi-agent Systems in Dynamic Environments. 284-293
  

Machine Learning, Classi?er Systems, Image Processing

• Alexandros Agapitos, Michael O'Neill, Anthony Brabazon:
  Evolutionary Learning of Technical Trading Rules without Data-Mining Bias. 294-303
  
• Faraz Ahmed, Farrukh Shahzad, Muddassar Farooq:
  Using Computational Intelligence to Identify Performance Bottlenecks in a Computer System. 304-313
  
• Bernd Bischl, Igor Vatolkin, Mike Preuss:
  Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation. 314-323
  
• Marcin Czajkowski, Marek Kretowski:
  Globally Induced Model Trees: An Evolutionary Approach. 324-333
  
• Pierre Delarboulas, Marc Schoenauer, Michèle Sebag:
  Open-Ended Evolutionary Robotics: An Information Theoretic Approach. 334-343
  
• Mauro Dragoni, Antonia Azzini, Andrea Tettamanzi:
  A Novel Similarity-Based Crossover for Artificial Neural Network Evolution. 344-353
  
• Jason Gauci, Kenneth O. Stanley:
  Indirect Encoding of Neural Networks for Scalable Go. 354-363
  
• Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
  Comparison-Based Optimizers Need Comparison-Based Surrogates. 364-373
  
• Mitchell A. Potter, Christine Couldrey:
  A Cooperative Coevolutionary Approach to Partitional Clustering. 374-383
  
• Alan P. Reynolds, David W. Corne, Michael J. Chantler:
  Feature Selection for Multi-purpose Predictive Models: A Many-Objective Task. 384-393
  
• Stephen D. Turner, Scott M. Dudek, Marylyn D. Ritchie:
  Incorporating Domain Knowledge into Evolutionary Computing for Discovering Gene-Gene Interaction. 394-403
  
• Ryan J. Urbanowicz, Jason H. Moore:
  The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies. 404-413
  
• Franck Patrick Vidal, Evelyne Lutton, Jean Louchet, Jean-Marie Rocchisani:
  Threshold Selection, Mitosis and Dual Mutation in Cooperative Co-evolution: Application to Medical 3D Tomography. 414-423
  
• Yanghui Wu, John A. W. McCall, David Corne:
  Comparative Analysis of Search and Score Metaheuristics for Bayesian Network Structure Learning Using Node Juxtaposition Distributions. 424-433
  
• Laleh Zangeneh, Peter J. Bentley:
  Analyzing the Credit Default Swap Market Using Cartesian Genetic Programming. 434-444
  

Memetic Algorithms, Hybridized Techniques, Meta and Hyperheurisics

• Jhon Edgar Amaya, Carlos Cotta, Antonio José Fernández Leiva:
  A Memetic Cooperative Optimization Schema and Its Application to the Tool Switching Problem. 445-454
  
• Juan C. Burguillo-Rial, Ana Peleteiro-Ramallo:
  Ownership and Trade in Spatial Evolutionary Memetic Games. 455-464
  
• Edmund K. Burke, Qiang Guo, Graham Kendall:
  A Hyper-Heuristic Approach to Strip Packing Problems. 465-474
  
• Aleksander Byrski, Robert Schaefer, Maciej Smolka, Carlos Cotta:
  Asymptotic Analysis of Computational Multi-Agent Systems. 475-484
  
• Madalina M. Drugan, Dirk Thierens:
  Path-Guided Mutation for Stochastic Pareto Local Search Algorithms. 485-495
  
• Jonathon Gibbs, Graham Kendall, Ender Özcan:
  Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics. 496-505
  
• David Iclanzan, Dumitru Dumitrescu:
  Graph Clustering Based Model Building. 506-515
  
• Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Yoshihiko Wakamatsu, Yusuke Nojima:
  How to Choose Solutions for Local Search in Multiobjective Combinatorial Memetic Algorithms. 516-525
  
• Joanna Kolodziej, Fatos Xhafa, Marcin Bogdanski:
  Secure and Task Abortion Aware GA-Based Hybrid Metaheuristics for Grid Scheduling. 526-535
  
• Yuichi Nagata, Shigenobu Kobayashi:
  A Memetic Algorithm for the Pickup and Delivery Problem with Time Windows Using Selective Route Exchange Crossover. 536-545
  
• Zhilei Ren, He Jiang, Jifeng Xuan, Zhongxuan Luo:
  Ant Based Hyper Heuristics with Space Reduction: A Case Study of the p-Median Problem. 546-555
  
• Yang Wang, Zhipeng Lü, Jin-Kao Hao:
  A Study of Multi-parent Crossover Operators in a Memetic Algorithm. 556-565
  
• Darrell Whitley, Doug Hains, Adele E. Howe:
  A Hybrid Genetic Algorithm for the Traveling Salesman Problem Using Generalized Partition Crossover. 566-575
  
• Saúl Zapotecas Martínez, Carlos A. Coello Coello:
  A Memetic Algorithm with Non Gradient-Based Local Search Assisted by a Meta-model. 576-585
  

Multiobjective Optimization, Theoretical Aspects

• Anne Auger, Johannes Bader, Dimo Brockhoff:
  Theoretically Investigating Optimal µ-Distributions for the Hypervolume Indicator: First Results for Three Objectives. 586-596
  
• Nicola Beume, Marco Laumanns, Günter Rudolph:
  Convergence Rates of (1+1) Evolutionary Multiobjective Optimization Algorithms. 597-606
  
• Karl Bringmann, Tobias Friedrich:
  Tight Bounds for the Approximation Ratio of the Hypervolume Indicator. 607-616
  
• Ewa Gajda, Robert Schaefer, Maciej Smolka:
  Evolutionary Multiobjective Optimization Algorithm as a Markov System. 617-626
  
• Tobias Glasmachers, Tom Schaul, Jürgen Schmidhuber:
  A Natural Evolution Strategy for Multi-objective Optimization. 627-636
  
• He Jiang, Shuyan Zhang, Zhilei Ren:
  Solving Multiobjective Optimization Problem by Constraint Optimization. 637-646
  
• Miqing Li, Jinhua Zheng, Ke Li, Qizhao Yuan, Ruimin Shen:
  Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization. 647-656
  
• Antonio López Jaimes, Hernán E. Aguirre, Kiyoshi Tanaka, Carlos A. Coello Coello:
  Objective Space Partitioning Using Conflict Information for Many-Objective Optimization. 657-666
  
• Frank Neumann, Madeleine Theile:
  How Crossover Speeds Up Evolutionary Algorithms for the Multi-criteria All-Pairs-Shortest-Path Problem. 667-676
  
• Joseph M. Pasia, Hernán E. Aguirre, Kiyoshi Tanaka:
  Path Relinking on Many-Objective NK-Landscapes. 677-686
  
• Pradyumn Kumar Shukla, Christian Hirsch, Hartmut Schmeck:
  In Search of Equitable Solutions Using Multi-objective Evolutionary Algorithms. 687-696
  
• Marcin Studniarski:
  Stopping Criteria for Genetic Algorithms with Application to Multiobjective Optimization. 697-706
  
• Tamara Ulrich, Johannes Bader, Lothar Thiele:
  Defining and Optimizing Indicator-Based Diversity Measures in Multiobjective Search. 707-717
  
• Tobias Wagner, Michael Emmerich, André H. Deutz, Wolfgang Ponweiser:
  On Expected-Improvement Criteria for Model-based Multi-objective Optimization. 718-727
  
• Simon Wessing, Nicola Beume, Günter Rudolph, Boris Naujoks:
  Parameter Tuning Boosts Performance of Variation Operators in Multiobjective Optimization. 728-737
  

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