Evolutionary Computation in Dynamic and Uncertain Environments 2007

• Shengxiang Yang:
  Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments. 3-28
  
• Tim Blackwell:
  Particle Swarm Optimization in Dynamic Environments. 29-49
  
• Lutz Schönemann:
  Evolution Strategies in Dynamic Environments. 51-77
  
• Sanyou Y. Zeng, Hui Shi, Lishan Kang, Lixin Ding:
  Orthogonal Dynamic Hill Climbing Algorithm: ODHC. 79-104
  
• Renato Tinós, Shengxiang Yang:
  Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments. 105-127
  
• Peter A. N. Bosman:
  Learning and Anticipation in Online Dynamic Optimization. 129-152
  
• Hai Huong Dam, Chris Lokan, Hussein A. Abbass:
  Evolutionary Online Data Mining: An Investigation in a Dynamic Environment. 153-178
  
• Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, Constantin Chiriac:
  Adaptive Business Intelligence: Three Case Studies. 179-196
  
• Frederico Paiva Quintão, Fabíola Guerra Nakamura, Geraldo Robson Mateus:
  Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks. 197-222
  
• Lars Gräning, Yaochu Jin, Bernhard Sendhoff:
  Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization. 225-250
  
• Wenbin Song:
  Evolutionary Shape Optimization Using Gaussian Processes. 251-267
  
• Margarita Reyes Sierra, Carlos A. Coello Coello:
  A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer. 269-296
  
• Kalyanmoy Deb, Pawan K. S. Nain:
  An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks. 297-322
  
• Yolanda Mack, Tushar Goel, Wei Shyy, Raphael T. Haftka:
  Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design. 323-342
  
• Ferrante Neri, Raino A. E. Mäkinen:
  Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation. 345-369
  
• Kagan Tumer, Adrian K. Agogino:
  Evolving Multi Rover Systems in Dynamic and Noisy Environments. 371-387
  
• Yoel Tenne, Steven William Armfield:
  A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions. 389-415
  
• Masaru Tezuka, Masaharu Munetomo, Kiyoshi Akama:
  Genetic Algorithm to Optimize Fitness Function with Sampling Error and its Application to Financial Optimization Problem. 417-434
  
• Dudy Lim, Yew-Soon Ong, Meng-Hiot Lim, Yaochu Jin:
  Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty. 437-456
  
• Chi Keong Goh, Kay Chen Tan:
  Evolving the Tradeoffs between Pareto-Optimality and Robustness in Multi-Objective Evolutionary Algorithms. 457-478
  
• Jianjun Hu, Shaobo Li, Erik D. Goodman:
  Evolutionary Robust Design of Analog Filters Using Genetic Programming. 479-496
  
• Hisashi Handa, Lee Chapman, Xin Yao:
  Robust Salting Route Optimization Using Evolutionary Algorithms. 497-517
  
• Zhun Fan, Jiachuan Wang, Min Wen, Erik D. Goodman, Ronald C. Rosenberg:
  An Evolutionary Approach For Robust Layout Synthesis of MEMS. 519-542
  
• Claudio M. Rocco Sanseverino, Daniel E. Salazar Aponte:
  A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs. 543-564
  
• Carlos Barrico, Carlos Henggeler Antunes:
  An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models. 565-582
  
• Qing Ling, Gang Wu, Qiuping Wang:
  Deterministic Robust Optimal Design Based on Standard Crowding Genetic Algorithm. 583-598