4. EMO 2006: Matsushima, Japan

Invited Talks

• Hirotaka Nakayama:
  Aspiration Level Methods in Interactive Multi-objective Programming and Their Engineering Applications (Abstract of Invited Talk). 1
  
• Kay Chen Tan:
  Improving the Efficacy of Multi-objective Evolutionary Algorithms for Real-World Applications (Abstract of Invited Talk). 2
  
• Carlos M. Fonseca:
  Decision Making in Evolutionary Optimization (Abstract of Invited Talk). 3
  
• Gary B. Lamont:
  MOEAs in the Design of Network Centric Systems (Abstract of Invited Talk). 4
  

Algorithm Design

• Hiroyuki Sato, Hernán E. Aguirre, Kiyoshi Tanaka:
  Controlling Dominance Area of Solutions and Its Impact on the Performance of MOEAs. 5-20
  
• Christian Grimme, Joachim Lepping:
  Designing Multi-objective Variation Operators Using a Predator-Prey Approach. 21-35
  
• Günter Rudolph, Boris Naujoks, Mike Preuss:
  Capabilities of EMOA to Detect and Preserve Equivalent Pareto Subsets. 36-50
  
• Hisao Ishibuchi, Yusuke Nojima:
  Optimization of Scalarizing Functions Through Evolutionary Multiobjective Optimization. 51-65
  
• Kalyanmoy Deb, Dhanesh Padmanabhan, Sulabh Gupta, Abhishek Kumar Mall:
  Reliability-Based Multi-objective Optimization Using Evolutionary Algorithms. 66-80
  
• Michael Kirley, Robert L. Stewart:
  Multiobjective Evolutionary Algorithms on Complex Networks. 81-95
  
• Pradyumn Kumar Shukla:
  On Gradient Based Local Search Methods in Unconstrained Evolutionary Multi-objective Optimization. 96-110
  

Algorithm Improvements

• Martin Lukasiewycz, Michael Glaß, Christian Haubelt, Jürgen Teich:
  Symbolic Archive Representation for a Fast Nondominance Test. 111-125
  
• Antonio J. Nebro, Juan José Durillo, Francisco Luna, Bernabé Dorronsoro, Enrique Alba:
  Design Issues in a Multiobjective Cellular Genetic Algorithm. 126-140
  
• Hamidreza Eskandari, Christopher D. Geiger, Gary B. Lamont:
  FastPGA: A Dynamic Population Sizing Approach for Solving Expensive Multiobjective Optimization Problems. 141-155
  
• Ken Harada, Jun Sakuma, Isao Ono, Shigenobu Kobayashi:
  Constraint-Handling Method for Multi-objective Function Optimization: Pareto Descent Repair Operator. 156-170
  
• Christian Igel, Thorsten Suttorp, Nikolaus Hansen:
  Steady-State Selection and Efficient Covariance Matrix Update in the Multi-objective CMA-ES. 171-185
  
• Mark P. Kleeman, Gary B. Lamont, Adam Cooney, Thomas R. Nelson:
  A Multi-tiered Memetic Multiobjective Evolutionary Algorithm for the Design of Quantum Cascade Lasers. 186-200
  
• Tadahiko Murata, Ryota Itai:
  Local Search in Two-Fold EMO Algorithm to Enhance Solution Similarity for Multi-objective Vehicle Routing Problems. 201-215
  
• Kenji Kobayashi, Tomoyuki Hiroyasu, Mitsunori Miki:
  Mechanism of Multi-Objective Genetic Algorithm for Maintaining the Solution Diversity Using Neural Network. 216-226
  

Alternative Methods

• Yi Jack Yau, Jason Teo, Patricia Anthony:
  Pareto Evolution and Co-evolution in Cognitive Game AI Synthesis. 227-241
  
• Peter Dawson, Geoffrey T. Parks, Daniel Jaeggi, Arturo Molina-Cristobal, P. John Clarkson:
  The Development of a Multi-threaded Multi-objective Tabu Search Algorithm. 242-256
  
• Tea Tusar, Bogdan Filipic:
  Differential Evolution versus Genetic Algorithms in Multiobjective Optimization. 257-271
  
• Gregorio Toscano Pulido, Carlos A. Coello Coello, Luis V. Santana-Quintero:
  EMOPSO: A Multi-Objective Particle Swarm Optimizer with Emphasis on Efficiency. 272-285
  
• Zhihua Cai, Wenyin Gong, Yongqin Huang:
  A Novel Differential Evolution Algorithm Based on epsilon -Domination and Orthogonal Design Method for Multiobjective Optimization. 286-301
  
• Swee Chiang Chiam, Kay Chen Tan, Abdullah Al Mamun:
  Molecular Dynamics Optimizer. 302-316
  

Applications

• Yeboon Yun, Hirotaka Nakayama, Min Yoon:
  Sequential Approximation Method in Multi-objective Optimization Using Aspiration Level Approach. 317-329
  
• Robert Cook, Arturo Molina-Cristobal, Geoffrey T. Parks, Cuitlahuac Osornio Correa, P. John Clarkson:
  Multi-objective Optimisation of a Hybrid Electric Vehicle: Drive Train and Driving Strategy. 330-345
  
• Swee Chiang Chiam, Kay Chen Tan, Abdullah Al Mamun:
  Multiobjective Evolutionary Neural Networks for Time Series Forecasting. 346-360
  
• Andy Pryke, Sanaz Mostaghim, Alireza Nazemi:
  Heatmap Visualization of Population Based Multi Objective Algorithms. 361-375
  
• In-Hee Lee, Soo-Yong Shin, Byoung-Tak Zhang:
  Multiplex PCR Assay Design by Hybrid Multiobjective Evolutionary Algorithm. 376-385
  
• Arnaud Liefooghe, Matthieu Basseur, Laetitia Jourdan, El-Ghazali Talbi:
  ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization. 386-400
  
• Dilip Datta, Kalyanmoy Deb, Carlos M. Fonseca:
  Multi-objective Evolutionary Algorithms for Resource Allocation Problems. 401-416
  
• Gustavo Sánchez, Minaya Villasana, Miguel Strefezza:
  Multi-objective Pole Placement with Evolutionary Algorithms. 417-427
  
• Waldo Cancino Ticona, Alexandre C. B. Delbem:
  A Multi-objective Evolutionary Approach for Phylogenetic Inference. 428-442
  
• Raziyeh Farmani, Dragan A. Savic, Godfrey A. Walters:
  On Convergence of Multi-objective Pareto Front: Perturbation Method. 443-456
  
• Arnaud Liefooghe, Matthieu Basseur, Laetitia Jourdan, El-Ghazali Talbi:
  Combinatorial Optimization of Stochastic Multi-objective Problems: An Application to the Flow-Shop Scheduling Problem. 457-471
  
• Hosang Jung, F. Frank Chen:
  Evolutionary Algorithm Based Corrective Process Control System in Glass Melting Process. 472-485
  
• Eduardo G. Carrano, Ricardo H. C. Takahashi, Carlos M. Fonseca, Oriane M. Neto:
  Bi-objective Combined Facility Location and Network Design. 486-500
  
• Joseph M. Pasia, Xavier Gandibleux, Karl F. Doerner, Richard F. Hartl:
  Local Search Guided by Path Relinking and Heuristic Bounds. 501-515
  
• Alan P. Reynolds, Beatriz de la Iglesia:
  Rule Induction for Classification Using Multi-objective Genetic Programming. 516-530
  
• Fermín Mallor-Gímenez, Rosa Blanco, Cristina Azcárate:
  Combining Linear Programming and Multiobjective Evolutionary Computation for Solving a Type of Stochastic Knapsack Problem. 531-545
  
• Yufeng Guo, Ed Keedwell, Godfrey A. Walters, Soon-Thiam Khu:
  Hybridizing Cellular Automata Principles and NSGAII for Multi-objective Design of Urban Water Networks. 546-559
  
• Rié Komuro, Joel H. Reynolds, E. David Ford:
  Using Multiobjective Evolutionary Algorithms to Assess Biological Simulation Models. 560-574
  

Engineering Design

• David Greiner, José M. Emperador, Gabriel Winter, Blas Galván:
  Improving Computational Mechanics Optimum Design Using Helper Objectives: An Application in Frame Bar Structures. 575-589
  
• Oliver Schütze, Laetitia Jourdan, Thomas Legrand, El-Ghazali Talbi, Jean Luc Wojkiewicz:
  A Multi-objective Approach to the Design of Conducting Polymer Composites for Electromagnetic Shielding. 590-603
  
• Rafal Kicinger, Shigeru Obayashi, Tomasz Arciszewski:
  Evolutionary Multiobjective Optimization of Steel Structural Systems in Tall Buildings. 604-618
  
• Mattia Ciprian, Valentino Pediroda, Carlo Poloni:
  Multi Criteria Decision Aiding Techniques to Select Designs After Robust Design Optimization. 619-632
  
• Silvia Poles, Paolo Geremia, F. Campos, S. Weston, M. Islam:
  MOGA-II for an Automotive Cooling Duct Optimization on Distributed Resources. 633-644
  
• Hirotaka Kaji, Hajime Kita:
  Individual Evaluation Scheduling for Experiment-Based Evolutionary Multi-objective Optimization. 645-659
  
• Shinya Watanabe, Kazutoshi Sakakibara:
  A Multiobjectivization Approach for Vehicle Routing Problems. 660-672
  
• Kokolo Ikeda, Hiromichi Suzuki, Sandor Markon, Hajime Kita:
  Designing Traffic-Sensitive Controllers for Multi-Car Elevators Through Evolutionary Multi-objective Optimization. 673-686
  
• Martin Josef Geiger, Wolf Wenger:
  On the Interactive Resolution of Multi-objective Vehicle Routing Problems. 687-699
  
• Evan J. Hughes:
  Radar Waveform Optimisation as a Many-Objective Application Benchmark. 700-714
  

Many Objectives

• André Sülflow, Nicole Drechsler, Rolf Drechsler:
  Robust Multi-Objective Optimization in High Dimensional Spaces. 715-726
  
• Mario Köppen, Kaori Yoshida:
  Substitute Distance Assignments in NSGA-II for Handling Many-objective Optimization Problems. 727-741
  
• Tobias Wagner, Nicola Beume, Boris Naujoks:
  Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization. 742-756
  
• Joshua D. Knowles, David Corne:
  Quantifying the Effects of Objective Space Dimension in Evolutionary Multiobjective Optimization. 757-771
  

Objective Handling

• Dhish Kumar Saxena, Kalyanmoy Deb:
  Non-linear Dimensionality Reduction Procedures for Certain Large-Dimensional Multi-objective Optimization Problems: Employing Correntropy and a Novel Maximum Variance Unfolding. 772-787
  
• Kalyanmoy Deb, Shamik Chaudhuri:
  I-MODE: An Interactive Multi-objective Optimization and Decision-Making Using Evolutionary Methods. 788-802
  
• Kalyanmoy Deb, Udaya Bhaskara Rao N., S. Karthik:
  Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling. 803-817
  
• Hirotaka Kaji, Hajime Kita:
  Acceleration of Experiment-Based Evolutionary Multi-objective Optimization Using Fitness Estimation. 818-831
  
• Aimin Zhou, Yaochu Jin, Qingfu Zhang, Bernhard Sendhoff, Edward P. K. Tsang:
  Prediction-Based Population Re-initialization for Evolutionary Dynamic Multi-objective Optimization. 832-846
  

Performance Assessments

• Gideon Avigad:
  multi-Multi-Objective Optimization Problem and Its Solution by a MOEA. 847-861
  
• Eckart Zitzler, Dimo Brockhoff, Lothar Thiele:
  The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration. 862-876
  
• Wolfgang Ponweiser, Markus Vincze:
  The Multiple Multi Objective Problem - Definition, Solution and Evaluation. 877-892
  
• Swee Chiang Chiam, Chi Keong Goh, Kay Chen Tan:
  Adequacy of Empirical Performance Assessment for Multiobjective Evolutionary Optimizer. 893-907
  
• Salem F. Adra, Ian Griffin, Peter J. Fleming:
  A Comparative Study of Progressive Preference Articulation Techniques for Multiobjective Optimisation. 908-921
  
• Michael T. M. Emmerich, André H. Deutz:
  Test Problems Based on Lamé Superspheres. 922-936
  
• Felipe Campelo, Frederico G. Guimarães, Hajime Igarashi:
  Overview of Artificial Immune Systems for Multi-objective Optimization. 937-951