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A Micro-Genetic Algorithm for Multiobjective Optimization

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

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.

Keywords

Pareto Front Multiobjective Optimization Crossover Rate External Memory Adaptive Grid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 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 (WSGoogle Scholar
  2. 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. 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. 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. 5.
    Kalyanmoy Deb. Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation, 7(3):205–230, Fall 1999.CrossRefGoogle Scholar
  6. 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. 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. 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. 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. 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. 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. 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.CrossRefGoogle Scholar
  13. 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. 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.CrossRefGoogle Scholar
  15. 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. 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.CrossRefGoogle Scholar
  17. 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. 18.
    J. David Schaffer. Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. PhD thesis, Vanderbilt University, 1984.Google Scholar
  19. 19.
    N. Srinivas and Kalyanmoy Deb. Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 2(3):221–248, fall 1994.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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. 22.
    Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2):173–195, Summer 2000.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  1. 1.Depto. de Ingeniería Eléctrica Secciíon de Computación Av. Instituto Politécnico Nacional No. 2508CINVESTAV-IPNMéxico
  2. 2.Maestrá en Inteligencia ArtificialLANIA-Universidad VeracruzanaXalapaMéxico

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