Diversity Maintenance Perspective: An Analysis of Exploratory Power and Function Optimization in the Context of Adaptive Genetic Algorithms

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)

Abstract

In order to increase the probability of finding optimal solution, GAs must maintain a balance between the exploration and exploitation. Maintaining population diversity not only prevents premature convergence but also provides a better coverage of the search space. Diversity measures are traditionally used to analyze evolutionary algorithms rather than guiding them. This chapter discusses the applicability of updation phase of binary trie coding scheme [BTCS] in introducing as well as maintaining population diversity. Here, the robustness of BTCS is compared with informed hybrid adaptive genetic algorithm (IHAGA), which works by adaptively changing the probabilities of crossover and mutation based on the fitness results of the respective offsprings in the next generation.

Keywords

Genetic algorithm Multidimensional knapsack problem  Diversity maintenance 

References

  1. 1.
    Sunanda, Garg M.L.: Binary trie coding scheme—An intelligent genetic algorithm avoiding premature convergence. Int. J. Comput. Math. Taylor & Francis. (2012). doi: 10.1080/00207160.2012.742514.
  2. 2.
    Yanqin, M., Jianchen W.: Improved hybrid adaptive genetic algorithm for solving knapsack problem. In: Proceedings of the 2nd International Conference and Information Processing, pp. 644–647 IEEE, (2011)Google Scholar
  3. 3.
    Sunanda, Garg, M.L.: GA implementation of the multi dimensional knapsack problem using compressed binary tries. Advances in computational research (ISSN: 0975–3273), 43–46 (2009)Google Scholar
  4. 4.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Publishing Company Inc, Massachusetts (1989)MATHGoogle Scholar
  5. 5.
    Schaffer, J.D., Carvana, R.A., Eshelman, L.J.: R. A study of control parameters affecting online performance of genetic algorithms for function optimization. In: Proceedings of the Third International Conference on Genetic Algorithms, Das (1989)Google Scholar
  6. 6.
    Ochoa, G., Harvey, I., Buxton, H.: On recombination and optimal mutation rates. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 488–495 (1999)Google Scholar
  7. 7.
    Srinivas, M., Parnaik, L.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst. Man. Cybern. 3, 1841–1844 (2003)Google Scholar
  8. 8.
    Vekaria, K., Clark, C.: Biases introduced by adaptive recombination operators. In: Proceedings of Genetic and Evolutionary Computation Conference, 670–677 (1999)Google Scholar
  9. 9.
    Ono, I., Kita, H., Kobayashi, S.: A robust real coded genetic algorithm using unimodal normal distribution crossover augmented by uniform crossover: Effects of self-adaptation of crossover probabilities. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 496–503 (1999)Google Scholar
  10. 10.
    Cervantes, J., Stephens, C.R.: Limitations of existing mutation rate heuristics and how a rank GA overcomes them. IEEE Trans. evol. comput. 13(2), 369–397 (2009)CrossRefGoogle Scholar
  11. 11.
    Liu, D., Feng, S.: A novel adaptive genetic algorithms. Proc. Int. Conf. Mach. Learn. Cybern. 1, 414–416 (2004)CrossRefGoogle Scholar
  12. 12.
    Zhu, K.: A diversity controlling adaptive genetic algorithm for the vehicle routing Problem with time windows. In: Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence, pp. 176–183 (2003)Google Scholar
  13. 13.
    Hagras, H., Pounds-Cornish, A., Cooley, M., Callaghan, V., Clarke, G.: Evolving spiking neural network controllers for autonomous robots. In: Proceedings IEEE International Conference on Robotics and Automation, vol. 5, pp. 4620–4626 (2004)Google Scholar
  14. 14.
    Zhang, J., Chung, H., Lo, W., et al.: Clustering based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Trans. Evol. Comput. 11(3), 326–335 (Jun. 2007)Google Scholar
  15. 15.
    Lobo, F. G., Lima, C. F., Michalewicz, Z (eds.): Parameter setting in evolutionary algorithms. volume 54 of Studies in Computational Intelligence Springer, (2007)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.School of Computer Science and Engineering, S.M.V.D.UKatraIndia

Personalised recommendations