Advertisement

Sequential Approximate Multiobjective Optimization Using Computational Intelligence

  • Authors
  • Min Yoon
  • Yeboon Yun
  • Hirotaka Nakayama

Part of the Vector Optimization book series (VECTOROPT)

Table of contents

  1. Front Matter
    Pages 1-14
  2. Hirotaka Nakayama, Yeboon Yun, Min Yoon
    Pages 1-15
  3. Hirotaka Nakayama, Yeboon Yun, Min Yoon
    Pages 17-43
  4. Hirotaka Nakayama, Yeboon Yun, Min Yoon
    Pages 45-71
  5. Hirotaka Nakayama, Yeboon Yun, Min Yoon
    Pages 73-112
  6. Hirotaka Nakayama, Yeboon Yun, Min Yoon
    Pages 113-149
  7. Hirotaka Nakayama, Yeboon Yun, Min Yoon
    Pages 151-168
  8. Hirotaka Nakayama, Yeboon Yun, Min Yoon
    Pages 169-183
  9. Back Matter
    Pages 1-13

About this book

Introduction

This book highlights a new direction of multiobjective optimzation, which has never been treated in previous publications. When the function form of objective functions is not known explicitly as encountered in many practical problems, sequential approximate optimization based on metamodels is an effective tool from a practical viewpoint. Several sophisticated methods for sequential approximate multiobjective optimization using computational intelligence are introduced along with real applications, mainly engineering problems, in this book.

Keywords

Evolutionary Multi-Objective Optimization Machine Learning Meta-Modelling Optimal Design of Experiments algorithms genetic algorithms modeling multi-objective optimization optimization

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-88910-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-540-88909-0
  • Online ISBN 978-3-540-88910-6
  • Series Print ISSN 1867-8971
  • Series Online ISSN 1867-898X
  • Buy this book on publisher's site