Skip to main content
SpringerLink
Log in
SpringerLink
Log in

A Micro-Genetic Algorithm for Multiobjective Optimization

  • Conference paper
  • First Online:
Evolutionary Multi-Criterion Optimization (EMO 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

Included in the following conference series:

Abstract

In this paper, we propose a multiobjective optimization approach based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population (four individuals were used in our experiment) and a reinitialization process. We use three forms of elitism and a memory to generate the initial population of the micro-GA. Our approach is tested with several standard functions found in the specialized literature. The results obtained are very encouraging, since they show that this simple approach can produce an important portion of the Pareto front at a very low computational cost.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. P. J. Bentley and J. P. Wakefield. Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms. In P. K. Chawdhry, R. Roy, and R. K. Pant, editors, Soft Computing in Engineering Design and Manufacturing, Part 5, pages 231–240, London, June 1997. Springer Verlag London Limited. (Presented at the 2nd On-line World Conference on Soft Computing in Design and Manufacturing (WS

    Google Scholar 

  2. Carlos C.H. Borges and Helio J.C. Barbosa. A Non-generational Genetic Algorithm for Multiobjective Optimization. In 2000 Congress on Evolutionary Computation, volume 1, pages 172–179, San Diego, California, July 2000. IEEE Service Center.

    Google Scholar 

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

    Google Scholar 

  4. Carlos A. Coello Coello. Handling Preferences in Evolutionary Multiobjective Optimization: A Survey. In 2000 Congress on Evolutionary Computation, volume 1, pages 30–37, Piscataway, New Jersey, July 2000. IEEE Service Center.

    Google Scholar 

  5. Kalyanmoy Deb. Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation, 7(3):205–230, Fall 1999.

    Article  Google Scholar 

  6. Kalyanmoy Deb, Samir Agrawal, Amrit Pratab, and T. Meyarivan. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. KanGAL report 200001, Indian Institute of Technology, Kanpur, India, 2000.

    Google Scholar 

  7. G. Dozier, J. Bowen, and D. Bahler. Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm. In Proceedings of the First IEEE Conference on Evolutionary Computation, pages 306–311, 1994.

    Google Scholar 

  8. David E. Goldberg. Sizing Populations for Serial and Parallel Genetic Algorithms. In J. David Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 70–79, San Mateo, California, 1989. Morgan Kaufmann Publishers.

    Google Scholar 

  9. Jeffrey Horn, Nicholas Nafpliotis, and David E. Goldberg. 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, June 1994. IEEE Service Center.

    Google Scholar 

  10. Hisao Ishibuchi and Tadahiko Murata. Multi-Objective Genetic Local Search Algorithm. In Toshio Fukuda and Takeshi Furuhashi, editors, Proceedings of the 1996 International Conference on Evolutionary Computation, pages 119–124, Nagoya, Japan, 1996. IEEE.

    Google Scholar 

  11. Andrzej Jaszkiewicz. Genetic local search for multiple objective combinatorial optimization. Technical Report RA-014/98, Institute of Computing Science, Poznan University of Technology, 1998.

    Google Scholar 

  12. E.G. Johnson and M.A.G. Abushagur. Micro-Genetic Algorithm Optimization Methods Applied to Dielectric Gratings. Journal of the Optical Society of America, 12(5):1152–1160, 1995.

    Article  Google Scholar 

  13. Charles L. Karr. Air-Injected Hydrocyclone Optimization via Genetic Algorithm. In Lawrence Davis, editor, Handbook of Genetic Algorithms, pages 222–236. Van Nostrand Reinhold, New York, 1991.

    Google Scholar 

  14. Joshua D. Knowles and David W. Corne. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2):149–172, 2000.

    Article  Google Scholar 

  15. K. Krishnakumar. Micro-genetic algorithms for stationary and non-stationary function optimization. In SPIE Proceedings: Intelligent Control and Adaptive Systems, pages 289–296, 1989.

    Google Scholar 

  16. Frank Kursawe. A variant of evolution strategies for vector optimization. In H. P. Schwefel and R. Männer, editors, Parallel Problem Solving from Nature. 1st Workshop, PPSN I, volume 496 of Lecture Notes in Computer Science, pages 193–197, Berlin, Germany, oct 1991. Springer-Verlag.

    Chapter  Google Scholar 

  17. Geoffrey T. Parks and I. Miller. Selective Breeding in a Multiobjective Genetic Algorithm. In A. E. Eiben, M. Schoenauer, and H.-P. Schwefel, editors, Parallel Problem Solving From Nature-PPSN V, pages 250–259, Amsterdam, Holland, 1998. Springer-Verlag.

    Google Scholar 

  18. J. David Schaffer. Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. PhD thesis, Vanderbilt University, 1984.

    Google Scholar 

  19. N. Srinivas and Kalyanmoy Deb. Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 2(3):221–248, fall 1994.

    Article  Google Scholar 

  20. David A. Van Veldhuizen and Gary B. Lamont. Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation, 8(2):125–147, 2000.

    Article  Google Scholar 

  21. Fengchao Xiao and Hatsuo Yabe. Microwave Imaging of Perfectly Conducting Cylinders from Real Data by Micro Genetic Algorithm Coupled with Deterministic Method. IEICE Transactions on Electronics, E81-C(12):1784–1792, December 1998.

    Google Scholar 

  22. Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2):173–195, Summer 2000.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Depto. de Ingeniería Eléctrica Secciíon de Computación Av. Instituto Politécnico Nacional No. 2508, CINVESTAV-IPN, D. F. 07300, Col. San Pedro Zacatenco, México

    Carlos A. Coello Coello Coello

  2. Maestrá en Inteligencia Artificial, LANIA-Universidad Veracruzana, 91090, Xalapa, Veracruz, México

    Gregorio Toscano Pulido

Authors
  1. Carlos A. Coello Coello Coello
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gregorio Toscano Pulido
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, ETH Zentrum, Gloriastrasse 35, 8092, Zurich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  2. Department of Mechanical Engineering Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, UP 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Electrical Engineering Department,Computer Science Section Av.Instituto Politecnico Nacional No.2508, CINVESTAV-IPN, Col.San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos Artemio Coello Coello

  4. Department of Computer Science, University of Reading, P.O.Box 225, Reading RG6 6AY, Whiteknights, UK

    David Corne

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Coello Coello Coello, C.A., Toscano Pulido, G. (2001). A Micro-Genetic Algorithm for Multiobjective Optimization. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_9

Download citation

  • DOI: https://doi.org/10.1007/3-540-44719-9_9

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation