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Comparing Two Constraint Handling Techniques in a Binary-Coded Genetic Algorithm for Optimization Problems

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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 relative performance of two constraint handling techniques, namely a parameter-less adaptive penalty method (APM) and the stochastic ranking method (SR), is studied in the context of continuous parameter constrained optimization problems. Both techniques are used within the same search engine, a binary-coded genetic algorithm.

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References

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Author information

Authors and Affiliations

  1. Laboratório Nacional de Computação Científica, Petrópolis, RJ, Brazil

    Helio J. C. Barbosa & Heder S. Bernardino

  2. Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil

    Afonso C. C. Lemonge

  3. Universidade Federal do Espírito Santo, São Mateus, ES, Brazil

    Leonardo G. Fonseca

Authors
  1. Helio J. C. Barbosa
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  2. Afonso C. C. Lemonge
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  3. Leonardo G. Fonseca
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  4. Heder S. Bernardino
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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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Barbosa, H.J.C., Lemonge, A.C.C., Fonseca, L.G., Bernardino, H.S. (2010). Comparing Two Constraint Handling Techniques in a Binary-Coded Genetic Algorithm for Optimization Problems. 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_13

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  • DOI: https://doi.org/10.1007/978-3-642-17298-4_13

  • 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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