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Simulated Evolution and Learning

6th International Conference, SEAL 2006, Hefei, China, October 15-18, 2006, Proceedings

  • Conference proceedings
  • © 2006

Overview

Editors:
  1. Tzai-Der Wang

    1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

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  2. Xiaodong Li

    1. School of Computer Science and information Technology, RMIT University, Melbourne, Australia

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  3. Shu-Heng Chen

    1. AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan

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  4. Xufa Wang

    1. Department of Computer Science and Technology, University of Sci. & Tech. of China, Hefei, Anhui, P.R. China

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  5. Hussein Abbass

    1. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

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  6. Hitoshi Iba

    1. The Univertity of Tokyo, Japan

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  7. Guo-Liang Chen

    1. Department of Computer, University of Science and Technology of China, Hefei, China

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  8. Xin Yao

    1. University of Birmingham, Birmingham, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4247)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: SEAL 2006.

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Table of contents (117 papers)

  1. Evolutionary Learning

    1. Numerical Optimization Using Organizational Particle Swarm Algorithm

      • Lin Cong, Yuheng Sha, Licheng Jiao
      Pages 150-157

    2. A Hybrid Discrete Particle Swarm Algorithm for Open-Shop Problems

      • Qingyun Yang, Jigui Sun, Juyang Zhang, Chunjie Wang
      Pages 158-165

    3. Particle Swarms Cooperative Optimization for Coalition Generation Problem

      • Guofu Zhang, Jianguo Jiang, Na Xia, Zhaopin Su
      Pages 166-173

    4. A Novel Multi-objective PSO Algorithm for Constrained Optimization Problems

      • Jingxuan Wei, Yuping Wang
      Pages 174-180

    5. A Hybrid Discrete Particle Swarm Optimization for the Traveling Salesman Problem

      • Xiangyong Li, Peng Tian, Jing Hua, Ning Zhong
      Pages 181-188

    6. Incremental Clustering Based on Swarm Intelligence

      • Bo Liu, Jiuhui Pan, R I (Bob) McKay
      Pages 189-196

    7. Variable Neighborhood Particle Swarm Optimization for Multi-objective Flexible Job-Shop Scheduling Problems

      • Hongbo Liu, Ajith Abraham, Okkyung Choi, Seong Hwan Moon
      Pages 197-204

    8. Optimal Designing of Multi-channel WDM Filter Using Intelligent Particle Swarm Optimization Algorithm

      • Yumin Liu, Zhongyuan Yu
      Pages 205-212

    9. Adaptive Comprehensive Learning Particle Swarm Optimizer with History Learning

      • J. J. Liang, P. N. Suganthan
      Pages 213-220

    10. Optimal Designing of EDFA Gain Flattening Long Period Fiber Grating by Intelligent Particle Swarm Optimization Algorithm

      • Yumin Liu, Zhongyuan Yu
      Pages 221-227

  2. Evolutionary Optimisation

    1. Research of Undirected Network Capacity Expansion Based on the Spanning-Tree

      • Yuhua Liu, Kaihua Xu, Hao Huang, Wei Teng
      Pages 228-235

    2. A New Approach to Solving Dynamic Traveling Salesman Problems

      • Changhe Li, Ming Yang, Lishan Kang
      Pages 236-243

    3. Shannon Wavelet Chaotic Neural Networks

      • Yao-qun Xu, Ming Sun, Ji-hong Shen
      Pages 244-251

    4. Deterministic Divide-and-Conquer Algorithm for Decomposable Black-Box Optimization Problems with Bounded Difficulty

      • Shude Zhou, Zengqi Sun
      Pages 252-260

    5. QPSO-Based QoS Multicast Routing Algorithm

      • Jun Sun, Jing Liu, Wenbo Xu
      Pages 261-268

    6. ANDYMARK: An Analytical Method to Establish Dynamically the Length of the Markov Chain in Simulated Annealing for the Satisfiability Problem

      • Juan Frausto-Solís, Héctor Sanvicente-Sánchez, Froilán Imperial-Valenzuela
      Pages 269-276

    7. An Accelerated Micro Genetic Algorithm for Numerical Optimization

      • Linsong Sun, Weihua Zhang
      Pages 277-283

    8. Optimal Ordering and Recovery Policy for Reusable Items with Shortages

      • H. M. Wee, Jonas C. P. Yu, Ling-Huey Su, Te-Chin Wu
      Pages 284-291

    9. A New Algorithm of Automatic Programming: GEGEP

      • Xin Du, Yueqiao Li, Datong Xie, Lishan Kang
      Pages 292-301

    10. Constrained Optimization Using Organizational Evolutionary Algorithm

      • Jing Liu, Weicai Zhong
      Pages 302-309
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  1. Simulated Evolution and Learning

Keywords

Editors and Affiliations

  • Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  • School of Computer Science and information Technology, RMIT University, Melbourne, Australia

    Xiaodong Li

  • AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

  • Department of Computer Science and Technology, University of Sci. & Tech. of China, Hefei, Anhui, P.R. China

    Xufa Wang

  • School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  • The Univertity of Tokyo, Japan

    Hitoshi Iba

  • Department of Computer, University of Science and Technology of China, Hefei, China

    Guo-Liang Chen

  • University of Birmingham, Birmingham, UK

    Xin Yao

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