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The Effect of Initial Population Sampling on the Convergence of Multi-Objective Genetic Algorithms

  • Silvia Poles
  • Yan Fu
  • Enrico Rigoni
Conference paper
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 618)

Abstract

This paper aims to demonstrate that the initial population plays an important role in the convergence of genetic algorithms independently from the algorithm and the problem. Using a well-distributed sampling increases the robustness and avoids premature convergence. The observation is proved using MOGA-II and NSGA-II with different sampling methods. This result is particularly important whenever the optimization involves time-consuming functions.

Keywords

Convergence Initial population MOGA-II, Multi-objective genetic algorithms NSGA-II 

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References

  1. 1.
    Affenzeller M, Wagner S (2004) The influence of population genetics for the redesign of genetic algorithm. J Syst Sci 30:41–49Google Scholar
  2. 2.
    Poles S (2003) MOGA-II: an improved multi-objective genetic algorithm. Technical report 2003–006, Esteco, TriesteGoogle Scholar
  3. 3.
    Deb K, Agrawal S, Pratab A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proceedings of the Parallel Problem Solving from Nature VI Conference. Lecture Notes in Computer Science No. 1917, Paris, France, Springer, pp 849–858Google Scholar
  4. 4.
    Haubelt C, Gamenik J, Teich J (2005) Initial, population construction for convergence improvement of MOEAs. In: Evolutionary Multi-Criterion Optimization Proceedings of the Third International Conference, EMO 2005, Guanajuato, MexicoGoogle Scholar
  5. 5.
    Hill R, Hiremath C (2005) Improving genetic algorithm convergence using problem structure and domain knowledge in multidimensional knapsack problems. Int J Oper Res 1(1/2):145– 159CrossRefGoogle Scholar
  6. 6.
    Hammersley JM (1960) Monte Carlo methods for solving multivariable problems. Ann N Y Acad Sci 86:844–874CrossRefGoogle Scholar
  7. 7.
    Sobol IM (1979) On the systematic search in a hypercube. SIAM J Numer Anal 16(5):790– 793CrossRefGoogle Scholar
  8. 8.
    Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geogr Inform Syst 4(3):313–332CrossRefGoogle Scholar
  9. 9.
    Iman RL, Shortencarier MJ (1984) A FORTRAN77 Program and User's Guide for Generation of Latin Hypercube and Random Samples for Use with Computer Models. NUREG/CR-3624, SAND83-2365, Sandia National Laboratories, CaliforniaGoogle Scholar
  10. 10.
    Kalagnanam JR, Diwekar UM (1997) An Efficient Sampling Technique for Off-line Quality Control. Technometrics 39(3):308–319CrossRefGoogle Scholar
  11. 11.
    Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput J 8(2):173–195CrossRefGoogle Scholar
  12. 12.
    Fonseca CM, Fleming PJ (1996) On the performance assessment and comparison of stochastic multiobjective optimizers. In: Voigt HM, Ebeling W, Rechenberg I, Schwefel HP (eds) Parallel problem solving from nature—PPSN IV, vol. 1141 of Lecture Notes in Computer Science. Springer, Berlin, pp 584–593Google Scholar
  13. 13.
    Knowles J (2005) A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers. Proceedings of the Fifth International Conference on Intelligent Systems Design and Applications (ISDA V), Wroclaw, PolandGoogle Scholar
  14. 14.
    Fonseca CM, Knowles J, Thiele L, Zitzler E (2005) A tutorial on the performance assessment of stochastic multiobjective optimizers. Tutorial at EMO 2005Google Scholar
  15. 15.
    Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—A comparative study. In: Voigt HM, Ebeling W, Rechenberg I, Schwefel HP (eds) Parallel problem solving from nature—PPSN IV, vol. 1141 of Lecture Notes in Computer Science. Springer, Berlin, pp 292–301Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Silvia Poles
    • 1
  • Yan Fu
    • 2
  • Enrico Rigoni
    • 1
  1. 1.ESTECO, Area Science Park—PadricianoItaly
  2. 2.Ford Motor Company MD 2115DearbornUSA

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