© 2020

High-Performance Simulation-Based Optimization

  • Thomas Bartz-Beielstein
  • Bogdan Filipič
  • Peter Korošec
  • El-Ghazali Talbi

Part of the Studies in Computational Intelligence book series (SCI, volume 833)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Many-Objective Optimization

    1. Front Matter
      Pages 1-1
    2. Michael T. M. Emmerich, Kaifeng Yang, André H. Deutz
      Pages 3-16
    3. Kalyan Shankar Bhattacharjee, Hemant Kumar Singh, Tapabrata Ray
      Pages 17-46
    4. Joachim van der Herten, Nicolas Knudde, Ivo Couckuyt, Tom Dhaene
      Pages 47-68
    5. Leonardo C. T. Bezerra, Manuel López-Ibáñez, Thomas Stützle
      Pages 69-92
    6. Hernán Aguirre, Kiyoshi Tanaka, Tea Tušar, Bogdan Filipič
      Pages 93-112
  3. Surrogate-Based Optimization

    1. Front Matter
      Pages 113-113
    2. Tinkle Chugh, Alma Rahat, Vanessa Volz, Martin Zaefferer
      Pages 137-163
    3. Tinkle Chugh, Chaoli Sun, Handing Wang, Yaochu Jin
      Pages 165-187
    4. Julien Pelamatti, Loïc Brevault, Mathieu Balesdent, El-Ghazali Talbi, Yannick Guerin
      Pages 189-224
    5. Jörg Stork, Martina Friese, Martin Zaefferer, Thomas Bartz-Beielstein, Andreas Fischbach, Beate Breiderhoff et al.
      Pages 225-244
  4. Parallel Optimization

    1. Front Matter
      Pages 245-245
    2. Raquel Hernández Gómez, Carlos A. Coello Coello, Enrique Alba
      Pages 247-273
    3. Nouredine Melab, Jan Gmys, Mohand Mezmaz, Daniel Tuyttens
      Pages 275-291

About this book


This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research.
That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.   


Computational Intelligence Many-Objective Optimization Surrogate-Based Optimization Parallel Optimization High-performance Algorithms Machine Learning

Editors and affiliations

  • Thomas Bartz-Beielstein
    • 1
  • Bogdan Filipič
    • 2
  • Peter Korošec
    • 3
  • El-Ghazali Talbi
    • 4
  1. 1.TH KölnCologneGermany
  2. 2.Jožef Stefan InstituteLjubljanaSlovenia
  3. 3.Jožef Stefan InstituteLjubljanaSlovenia
  4. 4.University LilleLilleFrance

Bibliographic information