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Evolutionary Computation in Dynamic and Uncertain Environments

  • Book
  • © 2007

Overview

Editors:
  1. Shengxiang Yang

    1. Department of Computer Science, University of Leicester, Leiceste, UK

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    You can also search for this editor in PubMed   Google Scholar

  2. Yew-Soon Ong

    1. School of Computer Engineering, Nanyang Technological University, Singapore

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    You can also search for this editor in PubMed   Google Scholar

  3. Yaochu Jin

    1. Honda Research Institute Europe, Offenbach am Main, Germany

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    You can also search for this editor in PubMed   Google Scholar

  • State of the art of evolutionary algorithms in dynamic and uncertain environments
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 51)

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Table of contents (26 chapters)

  1. Front Matter

    Pages I-XXIII
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  2. Optimum Tracking in Dynamic Environments

    1. Front Matter

      Pages 1-1
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    2. Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments

      • Shengxiang Yang
      Pages 3-28

    3. Particle Swarm Optimization in Dynamic Environments

      • Tim Blackwell
      Pages 29-49

    4. Evolution Strategies in Dynamic Environments

      • Lutz Schönemann
      Pages 51-77

    5. Orthogonal Dynamic Hill Climbing Algorithm: ODHC

      • Sanyou Zeng, Hui Shi, Lishan Kang, Lixin Ding
      Pages 79-104

    6. Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments

      • Renato Tinós, Shengxiang Yang
      Pages 105-127

    7. Learning and Anticipation in Online Dynamic Optimization

      • Peter A. N. Bosman
      Pages 129-152

    8. Evolutionary Online Data Mining: An Investigation in a Dynamic Environment

      • Hai H. Dam, Chris Lokan, Hussein A. Abbass
      Pages 153-178

    9. Adaptive Business Intelligence: Three Case Studies

      • Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, Constantin Chiriac
      Pages 179-196

    10. Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks

      • Frederico Paiva Quintão, Fabíola Guerra Nakamura, Geraldo Robson Mateus
      Pages 197-222

  3. Approximation of Fitness Functions

    1. Front Matter

      Pages 224-224
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    2. Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization

      • Lars Gräning, Yaochu Jin, Bernhard Sendhoff
      Pages 225-250

    3. Evolutionary Shape Optimization Using Gaussian Processes

      • Wenbin Song
      Pages 251-267

    4. A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer

      • Margarita Reyes-Sierra, Carlos A. Coello Coello
      Pages 269-296

    5. An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks

      • Kalyanmoy Deb, Pawan K. S. Nain
      Pages 297-322

    6. Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design

      • Yolanda Mack, Tushar Goel, Wei Shyy, Raphael Haftka
      Pages 323-342

  4. Handling Noisy Fitness Functions

    1. Front Matter

      Pages 344-344
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    2. Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation

      • Ferrante Neri, Raino A. E. Mäkinen
      Pages 345-369

    3. Evolving Multi Rover Systems in Dynamic and Noisy Environments

      • Kagan Tumer, Adrian Agogino
      Pages 371-387
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Keywords

About this book

Evolutionary computation is a class of problem optimization methodology with the inspiration from the natural evolution of species. In nature, the population of a species evolves by means of selection and variation. These two principles of natural evolution form the fundamental of evolutionary - gorithms (EAs). During the past several decades, EAs have been extensively studied by the computer science and arti?cial intelligence communities. As a classofstochasticoptimizationtechniques,EAscanoftenoutperformclassical optimization techniques for di?cult real world problems. Due to the ease of use and robustness, EAs have been applied to a wide variety of optimization problems. Most of these optimization problems ta- led are stationary and deterministic. However, many real-world optimization problems are subjected to dynamic and uncertain environments that are often impossible to avoid in practice. For example, the ?tness function is uncertain or noisy as a result of simulation errors, measurement errors or approximation errors. In addition, the design variables or environmental conditions may also perturb or change over time. For these dynamic and uncertain optimization problems, the objective of the EA is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic en- ronments, or to ?nd a robust solution that operates optimally in the presence of uncertainties. This poses serious challenges to classical optimization te- niques and conventional EAs as well. However, conventional EAs with proper enhancements are still good tools of choice for optimization problems in - namic and uncertain environments.

Editors and Affiliations

  • Department of Computer Science, University of Leicester, Leiceste, UK

    Shengxiang Yang

  • School of Computer Engineering, Nanyang Technological University, Singapore

    Yew-Soon Ong

  • Honda Research Institute Europe, Offenbach am Main, Germany

    Yaochu Jin

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