Evolutionary Multi-Criterion Optimization

9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings

  • Heike Trautmann
  • Günter Rudolph
  • Kathrin Klamroth
  • Oliver Schütze
  • Margaret Wiecek
  • Yaochu Jin
  • Christian Grimme

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10173)

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 10173)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Naveed Reza Aghamohammadi, Shaul Salomon, Yiming Yan, Robin C. Purshouse
    Pages 1-15
  3. Cristóbal Barba-Gonzaléz, José García-Nieto, Antonio J. Nebro, José F. Aldana-Montes
    Pages 16-30
  4. Leonardo C. T. Bezerra, Manuel López-Ibáñez, Thomas Stützle
    Pages 31-45
  5. Aymeric Blot, Alexis Pernet, Laetitia Jourdan, Marie-Éléonore Kessaci-Marmion, Holger H. Hoos
    Pages 61-76
  6. Marlon Braun, Pradyumn Shukla, Hartmut Schmeck
    Pages 88-102
  7. Dimo Brockhoff, Anne Auger, Nikolaus Hansen, Tea Tušar
    Pages 103-119
  8. Flávio Vinícius Cruzeiro Martins, João F. M. Sarubbi, Elizabeth F. Wanner
    Pages 120-134
  9. Oliver Cuate, Bilel Derbel, Arnaud Liefooghe, El-Ghazali Talbi, Oliver Schütze
    Pages 135-150
  10. Yves De Smet, Jean-Philippe Hubinont, Jean Rosenfeld
    Pages 151-159
  11. Kalyanmoy Deb, Rayan Hussein, Proteek Roy, Gregorio Toscano
    Pages 160-175
  12. Mario Garza-Fabre, Julia Handl, Joshua Knowles
    Pages 236-251
  13. Richard A. Gonçalves, Lucas M. Pavelski, Carolina P. de Almeida, Josiel N. Kuk, Sandra M. Venske, Myriam R. Delgado
    Pages 267-281

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Münster, Germany in March 2017. 

The 33 revised full papers presented together with 13 poster presentations were carefully reviewed and selected from 72 submissions. The EMO 2017 aims to discuss all aspects of EMO development and deployment, including theoretical foundations; constraint handling techniques; preference handling techniques; handling of continuous, combinatorial or mixed-integer problems; local search techniques; hybrid approaches; stopping criteria; parallel EMO models; performance evaluation; test functions and benchmark problems; algorithm selection approaches; many-objective optimization; large scale optimization; real-world applications; EMO algorithm implementations.

Keywords

big data evolutionary algorithms machine learning numeric computing parallel computing algorithm analysis and problem complexity artificial intelligence cluster analysis combinatoric problems computer applications evolutionary computation expert knowledge integration hybrid optimization model-based optimization multi-criteria decision making multi-objective optimization performance evaluation quality of service randomized search heuristics visualization

Editors and affiliations

  • Heike Trautmann
    • 1
  • Günter Rudolph
    • 2
  • Kathrin Klamroth
    • 3
  • Oliver Schütze
    • 4
  • Margaret Wiecek
    • 5
  • Yaochu Jin
    • 6
  • Christian Grimme
    • 7
  1. 1.University of MünsterMünsterGermany
  2. 2.TU Dortmund UniversityDortmundGermany
  3. 3.University of WuppertalWuppertalGermany
  4. 4.CINVESTAV-IPNMexico CityMexico
  5. 5.Clemson UniversityClemsonUSA
  6. 6.University of SurreyGuildfordUnited Kingdom
  7. 7.University of MünsterMünsterGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-54157-0
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-54156-3
  • Online ISBN 978-3-319-54157-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book