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The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2003)

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Abstract

In this paper, we deal with an important issue generally omitted in the current literature on evolutionary multiobjective optimization: on-line adaptation. We propose a revised version of our micro-GA for multiobjective optimization which does not require any parameter fine-tuning. Furthermore, we introduce in this paper a dynamic selection scheme through which our algorithm decides which is the “best’ crossover operator to be used at any given time. Such a scheme has helped to improve the performance of the new version of the algorithm which is called the micro-GA2 (μGA2). The new approach is validated using several test function and metrics taken from the specialized literature and it is compared to the NSGA-II and PAES.

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References

  1. Hussein A. Abbass. The Self-Adaptive Pareto Differential Evolution Algorithm. In Congress on Evolutionary Computation (CEC’2002), volume 1, pages 831–836, Piscataway, New Jersey, May 2002. IEEE Service Center.

    Google Scholar 

  2. Dirk Büche, Gianfranco Guidati, Peter Stoll, and Petros Kourmoursakos. Self-Organizing Maps for Pareto Optimization of Airfoils. In Juan Julián Merelo Guervós et al., editor, Parallel Problem Solving from Nature—PPSN VII, pages 122–131, Granada, Spain, September 2002. Springer-Verlag. Lecture Notes in Computer Science No. 2439.

    Chapter  Google Scholar 

  3. Carlos A. Coello Coello and Gregorio Toscano Pulido. Multiobjective Optimization using a Micro-Genetic Algorithm. In Lee Spector et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), pages 274–282, San Francisco, California, 2001. Morgan Kaufmann Publishers.

    Google Scholar 

  4. Carlos A. Coello Coello, David A. Van Veldhuizen, and Gary B. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, May 2002. ISBN 0-3064-6762-3.

    MATH  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, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, April 2002.

    Article  Google Scholar 

  7. Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler. Scalable Multi-Objective Optimization Test Problems. In Congress on Evolutionary Computation (CEC’2002), volume 1, pages 825–830, Piscataway, New Jersey, May 2002. IEEE Service Center.

    Google Scholar 

  8. 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 

  9. Teuvo Kohonen, T.S. Huang, and M.R. Schroeder, editors. Self-Organizing Maps. Springer-Verlag, 2001.

    Google Scholar 

  10. 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 

  11. Marco Laumanns, Günter Rudolph, and Hans-Paul Schwefel. Mutation Control and Convergence in Evolutionary Multi-Objective Optimization. In Proceedings of the 7th International Mendel Conference on Soft Computing (MENDEL 2001), Brno, Czech Republic, June 2001.

    Google Scholar 

  12. Jason R. Schott. Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, May 1995.

    Google Scholar 

  13. K.C. Tan, T.H. Lee, and E.F. Khor. Evolutionary Algorithms with Dynamic Population Size and Local Exploration for Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 5(6):565–588, December 2001.

    Article  Google Scholar 

  14. David A. Van Veldhuizen. Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio, May 1999.

    Google Scholar 

  15. David A. Van Veldhuizen and Gary B. Lamont. Multiobjective Evolutionary Algorithm Research: A History and Analysis. Technical Report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, 1998.

    Google Scholar 

  16. Zhong-Yao Zhu and Kwong-Sak Leung. Asynchronous Self-Adjustable Island Genetic Algorithm for Multi-Objective Optimization Problems. In Congress on Evolutionary Computation (CEC’2002), volume 1, pages 837–842, Piscataway, New Jersey, May 2002. IEEE Service Center.

    Google Scholar 

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Author information

Authors and Affiliations

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

    Gregorio Toscano Pulido & Carlos A. Coello Coello

Authors
  1. Gregorio Toscano Pulido
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  2. Carlos A. Coello Coello
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Centre for Intelligent Systems, University of the Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter J. Fleming

  3. Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Gloriastraße 35, 8092, Zürich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

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

    Kalyanmoy Deb

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© 2003 Springer-Verlag Berlin Heidelberg

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Toscano Pulido, G., Coello Coello, C.A. (2003). The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_18

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  • DOI: https://doi.org/10.1007/3-540-36970-8_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

  • eBook Packages: Springer Book Archive

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