Advertisement

LCS Based Diversity Maintenance in Adaptive Genetic Algorithms

  • Ryoma OhiraEmail author
  • Md. Saiful Islam
  • Jun Jo
  • Bela Stantic
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 996)

Abstract

A genetic algorithm (GA) experiences premature convergence when the diversity is lost in the population. Adaptive GAs aim to maintain diversity in the population by trading off a balance between exploring the problem space and exploiting known solutions. Existing metrics for population diversity measures only examine the similarity between individuals on a genetic level. However, similarities in the order of genes in individuals in ordered problems, such as the travelling salesman problem (TSP) can play an important role in effective diversity measures. By examining the similarities of individuals by the order of their genes, this paper proposes longest common subsequence (LCS) based metrics for measuring population diversity and its application in adaptive GAs for solving TSP. Extensive experimental results demonstrate the superiority of our proposal to existing approaches.

Keywords

Adaptive GA LCS Diversity maintenance TSP 

References

  1. 1.
    Eiben, Á.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 124–141 (1999)CrossRefGoogle Scholar
  2. 2.
    Chaiyaratana, N., Piroonratana, T., Sangkawelert, N.: Effects of diversity control in single-objective and multi-objective genetic algorithms. J. Heuristics 13(1), 1–34 (2007)CrossRefGoogle Scholar
  3. 3.
    Pan, Q.K., Suganthan, P.N., Wang, L., Gao, L., Mallipeddi, R.: A differential evolution algorithm with self-adapting strategy and control parameters. Comput. Oper. Res. 38(1), 394–408 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zhu, K.Q.: A diversity-controlling adaptive genetic algorithm for the vehicle routing problem with time windows. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, pp. 176–183. IEEE (2003)Google Scholar
  5. 5.
    Shimodaira, H.: A diversity-control-oriented genetic algorithm (DCGA): performance in function optimization. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 44–51. IEEE (2001)Google Scholar
  6. 6.
    Ursem, R.K.: Diversity-guided evolutionary algorithms. In: Guervós, J.J.M., Adamidis, P., Beyer, H.-G., Schwefel, H.-P., Fernández-Villacañas, J.-L. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 462–471. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45712-7_45CrossRefGoogle Scholar
  7. 7.
    Mc Ginley, B., Maher, J., O’Riordan, C., Morgan, F.: Maintaining healthy population diversity using adaptive crossover, mutation, and selection. IEEE Trans. Evol. Comput. 15(5), 692–714 (2011)CrossRefGoogle Scholar
  8. 8.
    Vidal, T., Crainic, T.G., Gendreau, M., Prins, C.: A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows. Comput. Oper. Res. 40, 475–489 (2013)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Segura, C., Hernandez, A., Luna, F., Alba, E.: Improving diversity in evolutionary algorithms: new best solutions for frequency assignment. IEEE Trans. Evol. Comput. 21(4), 539–553 (2017)CrossRefGoogle Scholar
  10. 10.
    Cruz-Salinas, A.F., Perdomo, J.G.: Self-adaptation of genetic operators through genetic programming techniques. In: GECCO, pp. 913–920. ACM (2017)Google Scholar
  11. 11.
    Adra, S.F., Fleming, P.J.: Diversity management in evolutionary many-objective optimization. IEEE Trans. Evol. Comput. 15, 183–195 (2011)CrossRefGoogle Scholar
  12. 12.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  13. 13.
    Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Found. Genet. Algorithms 1, 69–93 (1991)MathSciNetGoogle Scholar
  14. 14.
    Abdoun, O., Abouchabaka, J.: A comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem. Int. J. Comput. Appl. 31(11), 49–57 (2011)Google Scholar
  15. 15.
    Abdoun, O., Abouchabaka, J., Tajani, C.: Analyzing the performance of mutation operators to solve the travelling salesman problem. Int. J. Emerg. Sci. 2, 61–77 (2012)zbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ryoma Ohira
    • 1
    Email author
  • Md. Saiful Islam
    • 1
  • Jun Jo
    • 1
  • Bela Stantic
    • 1
  1. 1.School of Information and Communication TechnologyGriffith UniversitySouthportAustralia

Personalised recommendations