Simulated Evolution and Learning

11th International Conference, SEAL 2017, Shenzhen, China, November 10–13, 2017, Proceedings

  • Yuhui Shi
  • Kay Chen Tan
  • Mengjie Zhang
  • Ke Tang
  • Xiaodong Li
  • Qingfu Zhang
  • Ying Tan
  • Martin Middendorf
  • Yaochu Jin
Conference proceedings SEAL 2017

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 10593)

Table of contents

  1. Front Matter
    Pages I-XXII
  2. Evolutionary Optimisation

    1. Front Matter
      Pages 1-1
    2. YingYing Cao, Wei Chen, Shi Cheng, Yifei Sun, Qunfeng Liu, Yun Li et al.
      Pages 27-38
    3. Muhammad Sulaman, Xinye Cai, Mustafa Mısır, Zhun Fan
      Pages 39-50
    4. Jialong Shi, Qingfu Zhang, Bilel Derbel, Arnaud Liefooghe, Sébastien Verel
      Pages 62-74
    5. Yi Chen, Aimin Zhou, Rongfang Zhou, Peng He, Yong Zhao, Lihua Dong
      Pages 97-109
    6. Junhua Wu, Markus Wagner, Sergey Polyakovskiy, Frank Neumann
      Pages 110-121
    7. Jingda Deng, Qingfu Zhang, Hui Li
      Pages 122-133
    8. Jiajie Mo, Zhun Fan, Wenji Li, Yi Fang, Yugen You, Xinye Cai
      Pages 134-144
    9. Chen Wang, Hui Ma, Aaron Chen, Sven Hartmann
      Pages 170-183
  3. Evolutionary Multiobjective Optimisation

    1. Front Matter
      Pages 209-209

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL 2017, held in Shenzhen, China, in November 2017. 
The 85 papers presented in this volume were carefully reviewed and selected from 145 submissions. They were organized in topical sections named: evolutionary optimisation; evolutionary multiobjective optimisation; evolutionary machine learning; theoretical developments; feature selection and dimensionality reduction; dynamic and uncertain environments; real-world applications; adaptive systems; and swarm intelligence.

Keywords

machine learning artificial intelligence evolutionary computation genetic algorithms evolutionary algorithms particle swarm optimization multiobjective optimization genetic programming pareto principle computing methodologies search technologies modeling and simulation algorithm design algorithm analysis evolutionary optimization evolutionary learning

Editors and affiliations

  • Yuhui Shi
    • 1
  • Kay Chen Tan
    • 2
  • Mengjie Zhang
    • 3
  • Ke Tang
    • 4
  • Xiaodong Li
    • 5
  • Qingfu Zhang
    • 6
  • Ying Tan
    • 7
  • Martin Middendorf
    • 8
  • Yaochu Jin
    • 9
  1. 1.Southern University of Science and TechnologyShenzhenChina
  2. 2.City University of Hong KongHong KongHong Kong
  3. 3.Victoria University of WellingtonWellingtonNew Zealand
  4. 4.Southern University of Science and TechnologyShenzhenChina
  5. 5.RMIT UniversityMelbourneAustralia
  6. 6.City University of Hong KongKowloon TongHong Kong
  7. 7.Peking UniversityBeijingChina
  8. 8.University of LeipzigLeipzigGermany
  9. 9.University of SurreyGuildford, SurreyUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-68759-9
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-68758-2
  • Online ISBN 978-3-319-68759-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book