Computational Intelligence in Expensive Optimization Problems

  • Yoel Tenne
  • Chi-Keong Goh

Part of the Adaptation Learning and Optimization book series (ALO, volume 2)

Table of contents

  1. Front Matter
  2. Techniques for Resource-Intensive Problems

    1. Front Matter
      Pages 1-1
    2. Luis V. Santana-Quintero, Alfredo Arias Montaño, Carlos A. Coello Coello
      Pages 29-59
    3. David Ginsbourger, Rodolphe Le Riche, Laurent Carraro
      Pages 131-162
    4. Frederico Gadelha Guimarães, David Alister Lowther, Jaime Arturo Ramírez
      Pages 163-191
    5. Hirotaka Nakayama, Yeboon Yun, Masakazu Shirakawa
      Pages 249-264
  3. Techniques for High-Dimensional Problems

    1. Front Matter
      Pages 295-295
    2. Andrea Caponio, Anna V. Kononova, Ferrante Neri
      Pages 297-323
    3. João Paulo Queiroz dos Santos, Francisco Chagas de Lima Júnior, Rafael Marrocos Magalhães, Jorge Dantas de Melo, Adrião Duarte Doria Neto
      Pages 345-369
    4. Haldun Süral, Nur Evin Özdemirel, Ýlter Önder, Meltem Sönmez Turan
      Pages 371-396
    5. Marco Cococcioni, Beatrice Lazzerini, Francesco Marcelloni
      Pages 397-422
    6. Madeleine Davis-Moradkhan, Will Browne
      Pages 423-452

About this book


In modern science and engineering, laboratory experiments are replaced by high fidelity and computationally expensive simulations. Using such simulations reduces costs and shortens development times but introduces new challenges to design optimization process. Examples of such challenges include limited computational resource for simulation runs, complicated response surface of the simulation inputs-outputs, and etc.

Under such difficulties, classical optimization and analysis methods may perform poorly. This motivates the application of computational intelligence methods such as evolutionary algorithms, neural networks and fuzzy logic, which often perform well in such settings. This is the first book to introduce the emerging field of computational intelligence in expensive optimization problems. Topics covered include:

  • Dedicated implementations of evolutionary algorithms, neural networks and fuzzy logic.
  • Reduction of expensive evaluations (modelling, variable-fidelity, fitness inheritance).
  • Frameworks for optimization (model management, complexity control, model selection).
  • Parallelization of algorithms (implementation issues on clusters, grids, parallel machines).
  • Incorporation of expert systems and human-system interface.
  • Single and multiobjective algorithms.
  • Data mining and statistical analysis.
  • Analysis of real-world cases (such as multidisciplinary design optimization).

The edited book provides both theoretical treatments and real-world insights gained by experience, all contributed by leading researchers in the respective fields. As such, it is a comprehensive reference for researchers, practitioners, and advanced-level students interested in both the theory and practice of using computational intelligence for expensive optimization problems.


algorithm algorithms computational intelligence control data mining evolution evolutionary algorithm fuzzy intelligence model modeling neural network neural networks optimization simulation

Editors and affiliations

  • Yoel Tenne
    • 1
  • Chi-Keong Goh
    • 2
  1. 1.Department of Mechanical Engineering and Science-Faculty of EngineeringKyoto UniversityKyotoJapan
  2. 2.Advanced Technology Centre, Rolls-Royce Singapore Pte LtdSingapore

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-10700-9
  • Online ISBN 978-3-642-10701-6
  • Series Print ISSN 1867-4534
  • Series Online ISSN 1867-4542
  • About this book