© 2017

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
Conference proceedings EMO 2017

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. Jussi Hakanen, Joshua D. Knowles
    Pages 282-297
  2. Daniel Horn, Melanie Dagge, Xudong Sun, Bernd Bischl
    Pages 298-313
  3. Amin Ibrahim, Shahryar Rahnamayan, Miguel Vargas Martin, Kalyanmoy Deb
    Pages 314-328
  4. Pascal Kerschke, Christian Grimme
    Pages 329-343
  5. Marie-Eléonore Kessaci-Marmion, Clarisse Dhaenens, Jérémie Humeau
    Pages 344-358
  6. Ahmed Khalifa, Mike Preuss, Julian Togelius
    Pages 359-374
  7. Longmei Li, Iryna Yevseyeva, Vitor Basto-Fernandes, Heike Trautmann, Ning Jing, Michael Emmerich
    Pages 406-421
  8. Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Hernán Aguirre, Kiyoshi Tanaka
    Pages 422-437
  9. Renan Mendes, Elizabeth Wanner, Flávio Martins, João Sarubbi
    Pages 438-452
  10. Ana Amélia S. Pereira, Helio J. C. Barbosa, Heder S. Bernardino
    Pages 484-498
  11. Miriam Pescador-Rojas, Raquel Hernández Gómez, Elizabeth Montero, Nicolás Rojas-Morales, María-Cristina Riff, Carlos A. Coello Coello
    Pages 499-513
  12. Andreas Richter, Jascha Achenbach, Stefan Menzel, Mario Botsch
    Pages 514-528
  13. Haitham Seada, Mohamed Abouhawwash, Kalyanmoy Deb
    Pages 545-559
  14. Olivier Sobrie, Vincent Mousseau, Marc Pirlot
    Pages 575-589

About these proceedings


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.


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