Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

On Sweeping Operators for Reducing Premature Convergence of Genetic Algorithms

유전 알고리즘의 조기수렴 저감을 위한 연산자 소인방법 연구

  • 이홍규 (한국기술교육대학교 전기공학과)
  • Received : 2011.06.28
  • Accepted : 2011.10.15
  • Published : 2011.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

GA (Genetic Algorithms) are efficient for searching for global optima but may have some problems such as premature convergence, convergence to local extremum and divergence. These phenomena are related to the evolutionary operators. As population diversity converges to low value, the search ability of a GA decreases and premature convergence or converging to local extremum may occur but population diversity converges to high value, then genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we analyze the effects of the selection operator, crossover operator, and mutation operator on convergence properties, and propose the sweeping method of mutation probability and elitist propagation rate to maintain the diversity of the GA's population for getting out of the premature convergence. Results of simulation studies verify the feasibility of using these sweeping operators to avoid premature convergence and convergence to local extrema.

Keywords

Acknowledgement

Supported by : 한국기술교육대학교

References

  1. S. Nolfi and D. Floreano, Evolutionary Robotics, MIT Press, 2000.
  2. H.-K. Lee and G.-K. Lee, "Convergence properties of genetic algorithms," Proc. of the ISCA Int'l Conference on Computers and Their Applications, Seattle, pp. 172-176, Mar. 2004.
  3. B. Chakraborty, and P. Chaudhuri, "On the use of genetic algorithm with elitism in robust and nonparametric multivariate analysis," Austrian Journal of Statistics, vol. 32, no. 1&2, pp. 13-27, 2003.
  4. M. Pelikan, D. E. Goldberg, and E. Cantu-Paz, "Bayesian optimization algorithm, population sizing, and time to convergence," Genetic and Evolutionary Computation Conference, Las Vegas, NV, pp. 275-282, July, 2000.
  5. M. Affenzeller and S. Wagner, "SASEGASA: An evolutionary algorithm for retarding premature convergence by self-adaptive selection pressure steering," Computational Methods in Neural Modelling, Springer LNCS, vol. 2686, pp. 438-445, 2003. https://doi.org/10.1007/3-540-44868-3_56
  6. H.-K. Lee, D.-H. Lee, Z. Ran, G. Lee, and M. Lee, "On parameter selection for reducing premature convergence of genetic algorithms," 23rd International Conference on Computer and Their Applications in Industry and Engineering (CAINE-2010), pp. 214-219, Nov. 2010.
  7. T. Jones and S. Forrest, "Fitness distance correlation as a measure of problem difficulty for genetic algorithms," Proc. of the 6th International Conference on Genetic Algorithms, pp. 184-192, 1995.
  8. K.-R. Cho, S.-W. Back, and D.-W. Lee, "Fitness change of mission scheduling algorithm using genetic theory according to the control constants," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 16, no. 6, pp. 572-578, June 2010. https://doi.org/10.5302/J.ICROS.2010.16.6.572
  9. G. Rudolph, "Convergence analysis of canonical genetic algorithms," IEEE Trans. on Neural Networks: Special Issue on Evolutionary Computation, vol. 5, no. 1, pp. 96-101, Jan. 1994. https://doi.org/10.1109/72.265964
  10. G. Rudolph, "Self adaptive mutations lead to premature convergence," IEEE Trans. on Evolutionary Computation, vol. 5, no. 4, pp. 410-414, Aug. 2001. https://doi.org/10.1109/4235.942534
  11. D. Goldberg and J. Richardson, "Genetic algorithms with sharing for multimodal function optimization," Proc. of the 2nd International Conference on Genetic Algorithms and their Applications, pp. 41-49, 1987.
  12. S.-H. Bae and B.-R. Moon, "Mutation rates in the context of hybrid genetic algorithms", Proc. of the Genetic and Evolutionary Computation Conference, vol. 3103/2004, pp. 381-382, 2004.
  13. M. Srinivas and M. Patnaik, "Adaptive probabilities of crossover and mutation in genetic algorithms," IEEE Trans. on Systems, Man and Cybernetics, vol. 24, no. 4. pp. 656-667, Apr. 1994.
  14. J. Zhang and Y. Shang, "An improved multi-objective adaptive niche genetic algorithm based on pareto front," IEEE International Advance Computing Conference, pp. 300-304, Mar. 2009.
  15. H. Wang, Y. Liu, and S. Zeng, "Opposition-based particle swarm algorithm with cauchy mutation," IEEE Congress on Evolutionary Computation, pp. 4750-4756, Sep. 2007.
  16. M. Rocha and J. Neves, "Preventing premature convergence to local optima in genetic algorithms via random offspring generation," Proc. of the 12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems: Multiple Approaches to Intelligent Systems, vol. 1611/1999, pp. 127-136, 1999.
  17. W. Cedeno, V. Vemuri, and T. Slezak, "Multi-niche crowding in genetic algorithms and its application to the assembly of DNA restriction-fragments," Evolutionary Computation, vol. 2, pp. 321-345, 1995.
  18. Y. Zhang and H. Zhang, "A novel niche genetic algorithm for multimodal optimization," Journal of Convergence Information Technology, vol. 6, no. 6, pp. 255-262, June 2011. https://doi.org/10.4156/jcit.vol6.issue6.26
  19. D. E. Goldberg and K. Sastry, "A practical schema theorem for genetic algorithm design and tuning," Proc. of the Genetic and Evolutionary Computation Conference, pp. 328-335, 2001.
  20. Y. Gao, "An upper bound on the convergence rates of canonical genetic algorithms," Complexity International, vol. 5, pp. 1-9, 1998.
  21. D. Beasley, D. R. Bull, and R. R. Martin, "A sequential niche technique for multimodal function optimization," Evolutionary Computation, vol. 1, no. 2, pp. 101-125, 1993. https://doi.org/10.1162/evco.1993.1.2.101
  22. S. Nijssen and T. Back, "An analysis of behavior of evolutionary algorithms on trap functions," IEEE Trans. on Evolutionary Computation, vol. 7, no. 1, pp. 11- 22, Feb. 2003. https://doi.org/10.1109/TEVC.2002.806169
  23. P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger, and S. Tiwari, "Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization," Technical Report NCL-TR- 2005001, Natural Computing Laboratory, Department of Computer Science, National Chiao Tung University, May 2005.
  24. M. Son and B.-R. Moon, "Raising mutation rate in the context of hybrid genetic algorithms," Proc. of the 13th Annual Genetic and Evolutionary Computation Conference, pp. 157-158, July 2011.