Solving Exercise Generation Problems Using the Improved EGAL Metaheuristic Algorithm with Precedence Constraints

  • Blanka LángEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1135)


Exercise generation is a well-known problem worth investigating. In our former paper, we argue on that diversity should be a primary objective as well, and we propose a novel approach called EGAL to solve a well-known problem: to generate very different exercises to test students’ knowledge in a specific range of topics. We showed that focusing on diversity and fitness at the same time result in a better quality of solutions in the resulting population. In this publication presents how the previously developed diversity oriented harmony search metaheuristic algorithm (EGAL) can be applied for some problems if there are precedence relations between some tasks of the exercise. An example is presented where we would like to generate good quality and diverse exercises to test students’ knowledge about their basic programming skills. The behaviors of the problems for which the improved EGAL algorithm can be applied effectively are summarized.


Exercise generation problem Harmony search metaheuristic algorithm Precedence constraints 


  1. 1.
    Sadigh, D., Seshia, S.A., Gupta, M.: Automating exercise generation: a step towards meeting the MOOC challenge for embedded systems. In: Proceedings of the Workshop on Embedded Systems Education (WESE) (October 2012)Google Scholar
  2. 2.
    Almeida, J., Araujo, I., Brito, I., Carvalho, N., Machado, G., Pereira, R., Smirnov, G.: Exercise generation with the system Passarola (2013).
  3. 3.
    Almeida, J.J., Grande, E., Smirnov, G.: Exercise generation on language specification. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds.) Recent Advances in Information Systems and Technologies, WorldCIST 2017. Advances in Intelligent Systems and Computing, vol. 569. Springer, Cham (2017)Google Scholar
  4. 4.
    Láng, B., Kardkovács, Z.T.: Solving exercise generation problems by diversity oriented meta-heuristics. In: Cang, S., Wang, Y. (eds.) SKIMA: 2016 10th International Conference on Software, Knowledge, Information Management & Applications: Chengdu University of Information Technology, China, December 15–17, 2016, Chengdu, China, pp. 49–54. IEEE, Piscataway (2016). ISBN 978-1-5090-3298-3Google Scholar
  5. 5.
    Láng, B.: Solving exercise generation problems using the improved EGAL metaheuristic algorithm, under reviewing (2018)Google Scholar
  6. 6.
    Coello, C.A.C.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Methods Appl. Mech. Eng. 191(11–12), 1245–1287 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Ponsich, A., Azzaro-Pantel, C., Domenech, S., Pibouleau, L.: Mixed-integer nonlinear programming optimization strategies for batch plant design problems. Ind. Eng. Chem. Res. 46(3), 854–863 (2007)CrossRefGoogle Scholar
  8. 8.
    Yu, X., Gen, M.: Introduction to Evolutionary Algorithms. Decision Engineering. Springer, Berlin (2010)CrossRefGoogle Scholar
  9. 9.
    Ulrich, T., Bader, J., Thiele, L.: Defining and optimizing indicator based diversity measures in multiobjective search. In: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, Part I, pp. 707–717. Springer (2010)Google Scholar
  10. 10.
    Ulrich, T., Thiele, L.: Maximizing population diversity in single objective optimization. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 641–648. ACM, New York (2011)Google Scholar
  11. 11.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  12. 12.
    Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Proceedings of the 8th Parallel Problem Solving from Nature - PPSN VIII, LNCS, no. 3242, pp. 832–842. Springer (2004)Google Scholar
  13. 13.
    Goulart, F., Campelo, F.: Preference-guided evolutionary algorithms for many-objective optimization. Inf. Sci. 329, 236–255 (2016)CrossRefGoogle Scholar
  14. 14.
    Gutjahr, W.J.: A provably convergent heuristic for stochastic bicriteria integer programming. J. Heuristics 15, 227–258 (2009)CrossRefGoogle Scholar
  15. 15.
    Zadorojniy, A., Masin, M., Shir, O.M., Zeidner, L.: Algorithms for finding maximum diversity of design variables in multi-objective optimization. In: New Challenges in Systems Engineering and Architecting Conference on Systems Engineering Research (CSER). Procedia Computer Science, no. 8, pp. 171–176. Elsevier (2012)Google Scholar
  16. 16.
    Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194, 3902–3933 (2004)CrossRefGoogle Scholar
  17. 17.
    Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188, 1567–1579 (2007)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Dixon, L.C.W., Szego, G.P.: Towards Global Optimization. North Holland Publishing, Amsterdam (1975)zbMATHGoogle Scholar
  19. 19.
    Du, K.-L., Swamy, M.N.S.: Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature. Birkhäuser, Basel (2016)CrossRefGoogle Scholar
  20. 20.
    Rosenbrock, H.H.: An automatic method for finding the greatest or least value of a function. Comput. J. 3(3), 175–184 (1960)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Goldstein, A.A., Price, J.F.: On descent from local minima. Math. Comput. 25, 569–574 (1971)MathSciNetCrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Corvinus University of BudapestBudapestHungary

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