Skip to main content

Handling Constraints in Genetic Algorithms Using Dominance-based Tournaments

  • Conference paper
Book cover Adaptive Computing in Design and Manufacture V

Abstract

In this paper, we propose a constraint-handling approach for genetic algorithms which uses a dominance-based selection scheme. The proposed approach does not require the fine tuning of a penalty function and does not require extra mechanisms to maintain diversity in the population. The algorithm is validated using several test functions taken from the specialized literature on evolutionary optimization. The results obtained indicate that the approach can produce reasonably good results at low computational costs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Camponogara E. and Talukdar S. (1997) A Genetic Algorithm for Constrained and Multiobjective Optimization. In Jarmo T. Alander, editor, 3rd Nordic Workshop on Genetic Algorithms and Their Applications (3NWGA), pages 49–62, Vaasa, Finland. University of Vaasa.

    Google Scholar 

  2. Chankong V. and Haimes YY. (1983) Multiobjective Decision Making: Theory and Methodology. Systems Science and Engineering, North-Holland.

    MATH  Google Scholar 

  3. Coello C. (1999) A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques. Knowledge and Information Systems. An International Journal, 1(3):269–308.

    Google Scholar 

  4. Coello C. (2000) Treating Constraints as Objectives for Single-Objective Evolutionary Optimization. Engineering Optimization, 32(3):275–308.

    Article  Google Scholar 

  5. Deb K. and Agrawal S. (1999) A Niched-Penalty Approach for Constraint Handling in Genetic Algorithms. In Proceedings of the ICANNGA, Portoroz, Slovenia, 1999.

    Google Scholar 

  6. Fonseca C. and Fleming P. (1993) Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In Stephanie Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423, San Mateo, California. University of Illinois at Urbana-Champaign, Morgan Kauffman Publishers.

    Google Scholar 

  7. Fonseca C. and Fleming P. (1995) An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1):1–16.

    Article  Google Scholar 

  8. Horn J., Nafpliotis N. and Goldberg D. (1994) A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, volume 1, pages 82–87, Piscataway, New Jersey. IEEE Service Center.

    Google Scholar 

  9. Jiménez F. and Verdegay J. (1999) Evolutionary techniques for constrained optimization problems. In 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT′99), Aachen, Germany, Springer-Verlag.

    Google Scholar 

  10. Koziel S. and Michalewicz Z. (1999). Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization. Evolutionary Computation, 7(1): 19–44.

    Article  Google Scholar 

  11. Michalewicz Z. and Schoenauer M. (1996) Evolutionary Algorithms for Constrained Parameter Optimization Problems. Evolutionary Computation, 4(1): 1–32.

    Article  Google Scholar 

  12. Parmee IC. and Purchase G. (1994) The development of a directed genetic search technique for heavily constrained design spaces. In I.C. Parmee, editor, Adaptive Computing in Engineering Design and Control-′94, pages 97–102, Plymouth, UK. University of Plymouth.

    Google Scholar 

  13. Schaffer D. (1985) Multiple objective optimization with vector evaluated genetic algorithms. In Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pages 93–100. Lawrence Erlbaum.

    Google Scholar 

  14. Surry P. and Radcliffe NJ. (1997) The COMOGA Method: Constrained Optimisation by Multiobjective Genetic Algorithms. Control and Cybernetics, 26(3).

    Google Scholar 

  15. Surry P., Radcliffe NJ. and Boyd ID (1995) A Multi-Objective Approach to Constrained Optimisation of Gas Supply Networks: The COMOGA Method. In Terence C. Fogarty, editor, Evolutionary Computing. AISB Workshop. Selected Papers, Lecture Notes in Computer Science, pages 166–180. Springer-Verlag, Sheffield, U.K.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlos A. Coello Coello .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag London

About this paper

Cite this paper

Coello Coello, C.A., Mezura-Montes, E. (2002). Handling Constraints in Genetic Algorithms Using Dominance-based Tournaments. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture V. Springer, London. https://doi.org/10.1007/978-0-85729-345-9_23

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-345-9_23

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-605-9

  • Online ISBN: 978-0-85729-345-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics