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Generating Sequential Space-Filling Designs Using Genetic Algorithms and Monte Carlo Methods

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Simulated Evolution and Learning (SEAL 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6457))

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Abstract

In this paper, the authors compare a Monte Carlo method and an optimization-based approach using genetic algorithms for sequentially generating space-filling experimental designs. It is shown that Monte Carlo methods perform better than genetic algorithms for this specific problem.

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Author information

Authors and Affiliations

  1. University of Antwerp, 2020, Antwerp, Belgium

    Karel Crombecq

  2. Ghent University - IBBT, 9050, Ghent, Belgium

    Tom Dhaene

Authors
  1. Karel Crombecq
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  2. Tom Dhaene
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Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  2. Department of Computer Science andl Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Arnab Bhattacharya

  3. Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, 712302, Kharagpur, West Bengal, India

    Nirupam Chakraborti

  4. Department of Civil Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Partha Chakroborty  & Ashu Jain  & 

  5. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  6. Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Joydeep Dutta

  7. Department of Chemical Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Santosh K. Gupta

  8. Aspiring Minds, 24 Pusa Road, 110005, New Delhi, India

    Varun Aggarwal

  9. Warwick Business School, University of Warwick, CV4 7AL, Coventry, UK

    Jürgen Branke

  10. Department of Computer Science and Engineering, University of Nevada, 89557, Reno, NV, USA

    Sushil J. Louis

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

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© 2010 Springer-Verlag Berlin Heidelberg

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Crombecq, K., Dhaene, T. (2010). Generating Sequential Space-Filling Designs Using Genetic Algorithms and Monte Carlo Methods. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-17298-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17297-7

  • Online ISBN: 978-3-642-17298-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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