Advertisement

Visual Analysis of Genetic Algorithms While Solving 0-1 Knapsack Problem

  • B. P. SathyajitEmail author
  • C. Shunmuga Velayutham
Conference paper
First Online:
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 28)

Abstract

This paper presents heat map based visual analysis of Genetic Algorithm (GA) solving 0-1 Knapsack Problem (KP). The current work is a preliminary investigation to understand the search strategy of GA solving KP through visual means. A simple GA has been employed to solve 50, 100 and 500 items 0-1 KP. Heat map based visualization of best chromosomes shows clearly the explorative and exploitative search strategies of GA in conjunction with convergence characteristics. This paper demonstrates the potential of visualization to analyze and understand Evolutionary Algorithms (EA) in general.

Keywords

Genetic algorithm Heat maps 0-1 knapsack problem Exploration and exploitation Visualization Search strategy 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Eiben, A.E., Smith, J.E., et al.: Introduction to Evolutionary Computing, vol. 53. Springer, Berlin (2003)Google Scholar
  2. 2.
    Jeyakumar, G., Velayutham, C.S.: A comparative study on theoretical and empirical evolution of population variance of differential evolution variants. In: Asia-Pacific Conference on Simulated Evolution and Learning, pp. 75–79. Springer (2010)Google Scholar
  3. 3.
    Bezerianos, A., Chevalier, F., Dragicevic, P., Elmqvist, N., Fekete, J.D.: Graphdice: a system for exploring multivariate social networks. In: Computer Graphics Forum, vol. 29, pp. 863–872. Wiley Online Library (2010)Google Scholar
  4. 4.
    Cruz, A., Machado, P., Assunção, F., Leitão, A.: Elicit: evolutionary computation visualization. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 949–956. ACM (2015)Google Scholar
  5. 5.
    Hart, E., Ross, P.: GAVEL-a new tool for genetic algorithm visualization. IEEE Trans. Evol. Comput. 5(4), 335–348 (2001)Google Scholar
  6. 6.
    Kerren, A., Egger, T.: Eavis: a visualization tool for evolutionary algorithms. In: 2005 IEEE Symposium on Visual Languages and Human-Centric Computing, pp. 299–301. IEEE (2005)Google Scholar
  7. 7.
    Radhika, P., Velayutham, C.S.: Visualization-a potential alternative for analyzing differential evolution search. Intell. Syst. Technol. Appl. 1, 31 (2015)Google Scholar
  8. 8.
    Wu, A.S., De Jong, K.A., Burke, D.S., Grefenstette, J.J., Ramsey, C.L.: Visual analysis of evolutionary algorithms. In: Proceedings of the 1999 Congress on Evolutionary Computation, 1999. CEC 99, vol. 2, pp. 1419–1425. IEEE (1999)Google Scholar
  9. 9.
    Bullock, S., Bedau, M.A.: Exploring the dynamics of adaptation with evolutionary activity plots. Artif. Life 12(2), 193–197 (2006)CrossRefGoogle Scholar
  10. 10.
    Pohlheim, H.: Visualization of evolutionary algorithms-set of standard techniques and multidimensional visualization. In: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation, vol. 1, pp. 533–540. Morgan Kaufmann Publishers Inc. (1999)Google Scholar
  11. 11.
    Romero, G., Merelo, J., Castillo, P., Castellano, J., Arenas, M.G.: Genetic algorithm visualization using self-organizing maps. In: International Conference on Parallel Problem Solving from Nature, pp. 442–451. Springer (2002)Google Scholar
  12. 12.
    Pryke, A., Mostaghim, S., Nazemi, A.: Heatmap visualization of population based multi objective algorithms. In: International Conference on Evolutionary Multi- Criterion Optimization, pp. 361–375. Springer (2007)Google Scholar
  13. 13.
    Collins, T.D.: Visualizing evolutionary computation. In: Advances in Evolutionary Computing, pp. 95–116. Springer, Berlin (2003)Google Scholar
  14. 14.
    McDermott, J.: Visualising evolutionary search spaces. ACM SIGEVOlution 7(1), 2–10 (2014)CrossRefGoogle Scholar
  15. 15.
    Shao, Y., Xu, H., Yin, W.: Solve zero-one knapsack problem by greedy genetic algorithm. In: International Workshop on Intelligent Systems and Applications, 2009. ISA 2009, pp. 1–4. IEEE (2009)Google Scholar
  16. 16.
    Shen, W., Xu, B., Huang, J.p.: An improved genetic algorithm for 0-1 knapsack problems. In: 2011 Second International Conference on Networking and Distributed Computing (ICNDC), pp. 32–35. IEEE (2011)Google Scholar
  17. 17.
    Changdar, C., Mahapatra, G., Pal, R.K.: An improved genetic algorithm based approach to solve constrained knapsack problem in fuzzy environment. Expert Syst. Appl. 42(4), 2276–2286 (2015)CrossRefGoogle Scholar
  18. 18.
    He, J., Mitavskiy, B., Zhou, Y.: A theoretical assessment of solution quality in evolutionary algorithms for the knapsack problem. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 141–148. IEEE (2014)Google Scholar
  19. 19.
    Pisinger, D.: Where are the hard knapsack problems? Comput. Oper. Res. 32(9), 2271–2284 (2005)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG  2018

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

Personalised recommendations

Cite paper

Buy options