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Competitive Generation for Genetic Algorithms

  • Jung, Sung-Hoon (Department of Information and Communication Engineering, Hansung University)
  • Published : 2007.02.25

Abstract

A new operation termed competitive generation in the processes of genetic algorithms is proposed for accelerating the optimization speed of genetic algorithms. The competitive generation devised by considering the competition of sperms for fertilization provides a good opportunity for the genetic algorithms to approach global optimum without falling into local optimum. Experimental results with typical problems showed that the genetic algorithms with competitive generation are superior to those without the competitive generation.

Keywords

References

  1. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989
  2. C. L. Karr and E. J. Gentry, 'Fuzzy Control of pH Using Genetic Algorithms,' IEEE Transactions on Fuzzy Systems, Vol. 1, pp. 46-53, Jan. 1993 https://doi.org/10.1109/TFUZZ.1993.390283
  3. M. Srinivas and L. M. Patnaik, 'Genetic Algorithms: A Survey,' IEEE Computer Magazine, pp. 17-26, June 1994
  4. J. L. R. Filho and P. C. Treleaven, 'GeneticAlgorithm Programming Environments,' IEEE Computer Magazine, pp. 28-43, June 1994
  5. D. Beasley, D. R. Bull, and R. R. Martin, 'An Overview of Genetic Algorithms: Part 1, Fundamentals,' Technical Report obtained from http://home.ifi. uio.no/ jimtoer/GA_Overview 1.pdf
  6. D. B. Fogel, 'An Introduction to Simulated EVolutionary Optimization,' IEEE Transactions on Neural Networks, vol. 5, pp. 3-14, Jan. 1994 https://doi.org/10.1109/72.265956
  7. H. Szczerbicka and M. Becker, 'Genetic Algorithms: A Tool for Modelling, Simulation, and Optimization of Complex Systems,' Cybernetics and Systems: An International Journal, vol. 29, pp. 639-659, Aug. 1998 https://doi.org/10.1080/019697298125461
  8. R. Yang and I. Douglas, 'Simple Genetic Algorithm with Local Tuning: Efficient Global Optimizing Technique,' Journal of Optimization Theory and Applications, vol. 98, pp. 449-465, Aug. 1998 https://doi.org/10.1023/A:1022697719738
  9. C. Xudong, Q. Jingen, N. Guangzheng, Y. Shiyou, and Z. Mingliu, 'An Improved Genetic Algorithm for Global Optimization of Electromagnetic Problems,' IEEE Transactions on Magnetics, vol. 37, pp. 3579-3583, Sept. 2001 https://doi.org/10.1109/20.952666
  10. E. Alba and B. Dorronsoro, 'The exploration/exploitation tradeoff in dynamic cellular genetic algorithms,' IEEE Transactions on Evolutionary Computation, Vol. 9, pp. 126-142, Apr. 2005 https://doi.org/10.1109/TEVC.2005.843751
  11. V. K. Koumousis and C. Katsaras, 'A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance,' IEEE Transactions on EVolutionary Computation, vol. 10, pp. 19-28, Feb. 2006 https://doi.org/10.1109/TEVC.2005.860765
  12. L. Davis, 'Adapting Operator Probabilities in Genetic Algorithms,' in Proceedings of the 3rd International Conference on Genetic Algorithms and their Applications, pp. 61-69, 1989
  13. R. Hinterding, Z. Michalewicz, and A. E. Eiben, 'Adaptation in EVolutionary Computation: A Survey,' in Proceedings of the 4th IEEE International Conference on Evolutionary Computation, pp. 65-69, 1997
  14. A. Tuson and P. Ross, 'Adapting Operator Settings In Genetic Algorithms,' Evolutionary Computation, vol. 6, no. 2,pp. 161-184, 1998 https://doi.org/10.1162/evco.1998.6.2.161
  15. C. W. Ho, K. H. Lee, and K. S. Leung, 'A Genetic Algorithm Based on Mutation and Crossover with Adaptive Probabilities,' in Proceedings of the 1999 Congress on Evolutionary Computation, vol. 1, pp. 768-775, 1999
  16. S. H. Jung, 'Self-tuning of Operator Probabilities in Genetic Algorithms,' 전자공학회 논문지, vol. 37, pp. 29-44, Sept. 2000
  17. R. Hinterding, 'Gaussian Mutation and Self-adaption in Numeric Genetic Algorithms,' in Proceedings of the 2nd IEEE International Conference on Evolutionary Computation, pp. 384-389, 1995
  18. J. Andre, P. Siarry, and T. Dognon, 'An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization,' Advances in engineering software, vol. 32, no. 1, pp. 49-60, 2001 https://doi.org/10.1016/S0965-9978(00)00070-3