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Multiobjective Problem Solving from Nature

From Concepts to Applications

  • Book
  • © 2008

Overview

Editors:
  1. Joshua Knowles

    1. Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, UK

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  2. David Corne

    1. Room G39 Earl Mountbatten Building, Heriot-Watt University, Edinburgh EH14 4AS, UK

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    You can also search for this editor in PubMed   Google Scholar

  3. Kalyanmoy Deb

    1. Dept. of Business Technology, Helsinki School of Economics, FIN-00101 Helsinki, Finland
      Dept. of Mechanical Engineering, Indian Institute of Technology Kanpur, India

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Part of the book series: Natural Computing Series (NCS)

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Table of contents (18 chapters)

  1. Front Matter

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

    1. Front Matter

      Pages 1-1
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    2. Introduction: Problem Solving, EC and EMO

      • Joshua Knowles, David Corne, Kalyanmoy Deb
      Pages 1-28

  3. Exploiting Multiple Objectives: From Problems to Solutions

    1. Front Matter

      Pages 29-29
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    2. Multiobjective Optimization and Coevolution

      • Sevan Gregory Ficici
      Pages 31-52

    3. Constrained Optimization via Multiobjective Evolutionary Algorithms

      • Efrén Mezura-Montes, Carlos A. Coello Coello
      Pages 53-75

    4. Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms

      • Lam T. Bui, Minh-Ha Nguyen, Jürgen Branke, Hussein A. Abbass
      Pages 77-91

    5. Computational Studies of Peptide and Protein Structure Prediction Problems via Multiobjective Evolutionary Algorithms

      • Vincenzo Cutello, Giuseppe Narzisi, Giuseppe Nicosia
      Pages 93-114

    6. Can Single-Objective Optimization Profit from Multiobjective Optimization?

      • Frank Neumann, Ingo Wegener
      Pages 115-130

    7. Modes of Problem Solving with Multiple Objectives: Implications for Interpreting the Pareto Set and for Decision Making

      • Julia Handl, Joshua Knowles
      Pages 131-151

  4. Machine Learning with Multiple Objectives

    1. Front Matter

      Pages 153-153
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    2. Multiobjective Supervised Learning

      • Jonathan E. Fieldsend, Richard M. Everson
      Pages 155-176

    3. Reducing Bloat in GP with Multiple Objectives

      • Stefan Bleuler, Johannes Bader, Eckart Zitzler
      Pages 177-200

    4. Multiobjective GP for Human-Understandable Models: A Practical Application

      • Katya Rodríguez-Vázquez, Peter J. Fleming
      Pages 201-218

    5. Multiobjective Classification Rule Mining

      • Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima
      Pages 219-240

  6. Scaling up Multiobjective Optimization

    1. Front Matter

      Pages 305-305
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Keywords

About this book

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.

Editors and Affiliations

  • Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, UK

    Joshua Knowles

  • Room G39 Earl Mountbatten Building, Heriot-Watt University, Edinburgh EH14 4AS, UK

    David Corne

  • Dept. of Business Technology, Helsinki School of Economics, FIN-00101 Helsinki, Finland

    Kalyanmoy Deb

  • Dept. of Mechanical Engineering, Indian Institute of Technology Kanpur, India

    Kalyanmoy Deb

Bibliographic Information

  • Book Title: Multiobjective Problem Solving from Nature

  • Book Subtitle: From Concepts to Applications

  • Editors: Joshua Knowles, David Corne, Kalyanmoy Deb

  • Series Title: Natural Computing Series

  • DOI: https://doi.org/10.1007/978-3-540-72964-8

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2008

  • Hardcover ISBN: 978-3-540-72963-1Published: 05 December 2007

  • Softcover ISBN: 978-3-662-50119-1Published: 23 August 2016

  • eBook ISBN: 978-3-540-72964-8Published: 15 November 2007

  • Series ISSN: 1619-7127

  • Series E-ISSN: 2627-6461

  • Edition Number: 1

  • Number of Pages: XVI, 411

  • Topics: Artificial Intelligence, Optimization, Engineering Design, Theory of Computation

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