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Multiobjective Problem Solving from Nature

From Concepts to Applications

  • Joshua Knowles
  • David Corne
  • Kalyanmoy Deb
  • Deva Raj Chair

Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages I-XVI
  2. Introduction: Problem Solving, EC and EMO

    1. Front Matter
      Pages 1-1
    2. Joshua Knowles, David Corne, Kalyanmoy Deb
      Pages 1-28
  3. Exploiting Multiple Objectives: From Problems to Solutions

    1. Front Matter
      Pages 29-29
    2. Sevan Gregory Ficici
      Pages 31-52
    3. Efrén Mezura-Montes, Carlos A. Coello Coello
      Pages 53-75
    4. Lam T. Bui, Minh-Ha Nguyen, Jürgen Branke, Hussein A. Abbass
      Pages 77-91
  4. Machine Learning with Multiple Objectives

    1. Front Matter
      Pages 153-153
    2. Jonathan E. Fieldsend, Richard M. Everson
      Pages 155-176
    3. Stefan Bleuler, Johannes Bader, Eckart Zitzler
      Pages 177-200
    4. Katya Rodríguez-Vázquez, Peter J. Fleming
      Pages 201-218
    5. Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima
      Pages 219-240
  5. Multiple Objectives in Design and Engineering

  6. Scaling up Multiobjective Optimization

    1. Front Matter
      Pages 305-305
    2. Yaochu Jin, Aimin Zhou, Qingfu Zhang, Bernhard Sendhoff, Edward Tsang
      Pages 331-355
    3. Edwin D. de Jong, Anthony Bucci
      Pages 357-376
    4. Dimo Brockhoff, Dhish Kumar Saxena, Kalyanmoy Deb, Eckart Zitzler
      Pages 377-403
  7. Back Matter
    Pages 405-409

About this book

Introduction

Multiobjective problems involve several competing measures of solution quality, and multiobjective evolutionary algorithms (MOEAs) and multiobjective problem solving have become important topics of research in the evolutionary computation community over the past 10 years. This is an advanced text aimed at researchers and practitioners in the area of search and optimization.

The book focuses on how MOEAs and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concepts of multiobjective optimization can be used to reformulate and resolve problems in broad areas such as constrained optimization, coevolution, classification, inverse modelling and design. The book is distinguished from other texts on MOEAs in that it is not primarily about the algorithms, nor specific applications, but about the concepts and processes involved in solving problems using a multiobjective approach. Each chapter contributes to the central, deep concepts and themes of the book: evaluating the utility of the multiobjective approach; discussing alternative problem formulations; showing how problem formulation affects the search process; and examining solution selection and decision making.

The book will be of benefit to researchers, practitioners and graduate students engaged with optimization-based problem solving. For multiobjective optimization experts, the book is an up-to-date account of emerging and advanced topics; for others, the book indicates how the multiobjective approach can lead to fresh insights.

Keywords

Problem-solving algorithms classification decision making design engineering design evolution evolutionary algorithm learning machine learning modeling multi-objective optimization natural computing optimization problem solving

Editors and affiliations

  • Joshua Knowles
    • 1
  • David Corne
    • 2
  • Kalyanmoy Deb
    • 3
  • Deva Raj Chair
    • 4
  1. 1.Manchester Interdisciplinary BiocentreUniversity of ManchesterManchesterUK
  2. 2.Room G39 Earl Mountbatten BuildingHeriot-Watt UniversityEdinburgh EH14 4ASUK
  3. 3.Dept. of Business TechnologyHelsinki School of EconomicsFIN-00101 HelsinkiFinland
  4. 4.Dept. of Mechanical EngineeringIndian Institute of Technology KanpurIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-72964-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-72963-1
  • Online ISBN 978-3-540-72964-8
  • Series Print ISSN 1619-7127
  • Buy this book on publisher's site