Journal of Heuristics

, Volume 16, Issue 6, pp 911–930 | Cite as

Generating meta-heuristic optimization code using ADATE



Local Search based meta-heuristic methods for finding good solutions to hard combinatorial optimization problems have attained a lot of success, and a plethora of methods exist, each with its own successes, and also with its own parameter settings and other method-specific details. At the same time, experience is needed to implement highly competitive code, and some of the experience applied is not easy to quantify.

ADATE is a system to automatically generate code based on a set of input-output specifications, and can work in vastly different domains. It generates code in a subset of the programming language ML and works by searching for transformations of purely functional ML programs.

Code automatically generated by the ADATE system compares with state-of-the-art handcrafted meta-heuristic optimization code. In particular, the programs generated by ADATE target the move selection part of BOOP—Boolean Optimization Problems. The baseline is a highly successful Tabu Search implementation. Comparisons are made for versions running for a limited number of iterations, being suitable for applications needing a short response time. The computational results show that the ADATE system is able to generate highly competitive code that produces more optimal solutions to hard BOOP instances within given iteration limits than the previously published Tabu Search implementation. The automatically generated code also gives new insights into the general design of meta-heuristic mechanisms, and contains novel search mechanisms.

Code generation Optimization Local search 


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  1. Bader-El-Din, M.B., Poli, R.: Generating sat local-search heuristics using a gp hyper-heuristic framework. In: Proceedings of the 8th International Conference on Artificial Evolution. LNCS, vol. 4926, pp. 37–49. Springer, Berlin (2007) Google Scholar
  2. Burke, E.K., Kendall, G., Hart, J., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Glover, F., Kochenbergerm, G. (eds.) Handbook of Meta-Heuristics, Chap. 16, pp. 457–474 (2003) Google Scholar
  3. Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.: Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. In: Proceedings of the 9th ACM Genetic and Evolutionary Computation Conference (GECCO’07), London, UK, pp. 1559–1565 (2007) Google Scholar
  4. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: A classification of hyper-heuristics approaches. Computer Science Technical Report No. NOTTCS-TR-SUB-0907061259-5808, School of Computer Science and Information Technology, University of Nottingham (2009a) Google Scholar
  5. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: A survey of hyper-heuristics. Computer Science Technical Report No. NOTTCS-TR-SUB-0906241418-2747, School of Computer Science and Information Technology, University of Nottingham (2009b) Google Scholar
  6. Cook, S.A.: The complexity of theorem-proving procedures. In: Proceedings of the Third ACM Symposium on Theory of Computing, pp. 151–158 (1971) Google Scholar
  7. Davoine, T., Hammer, P.L., Vizvári, B.: A heuristic for Boolean optimization problems. J. Heuristics 9, 229–247 (2001) CrossRefGoogle Scholar
  8. Du, D., Gu, J., Pardalos, P.: Satisfiability Problem: Theory and Applications. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 35 (1997) Google Scholar
  9. Fukunaga, A.S.: Automated discovery of local search heuristics for satisfiability testing. Evol. Comput. 16(1), 31–61 (2008) CrossRefGoogle Scholar
  10. Geiger, C.D., Uzsoy, R., Aytug, H.: Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J. Sched. 9(1), 7–34 (2006) MATHCrossRefGoogle Scholar
  11. Hvattum, L.M., Løkketangen, A., Glover, F.: Adaptative memory search for Boolean optimization problems. Discrete Appl. Math. 142, 99–109 (2004). Special issue on Boolean and pseudo-Boolean functions MATHCrossRefMathSciNetGoogle Scholar
  12. Hvattum, L.M., Løkketangen, A., Glover, F.: New heuristics and adaptive memory procedures for Boolean optimization problems. In: Karlof, J. (ed.) Integer Programming: Theory and Practice, pp. 1–18. CRC Press, Boca Raton (2006) Google Scholar
  13. Geard, N., Wiles, J., Hallinan, J., Tonkes, B., Skellett, B.: A comparison of neutral landscapes—nk, nkp and nkq. In: Proceedings of the 2002 Congress on Evolutionary Computation (2002) Google Scholar
  14. Glover, F., Laguna, M.: Tabu Search. Springer, Berlin (1997) MATHGoogle Scholar
  15. Hoos, H.: On the run-time behaviour of stochastic local search methods for SAT. In: Proceedings of AAAI, pp. 661–666 (1969) Google Scholar
  16. Kimura, M.: The Neutral Theory of Molecular Evolution. Cambridge University Press, Cambridge (1983) CrossRefGoogle Scholar
  17. Linnaeus, C.: Systema Naturae per Regna Tria Naturae, Secundum Classes, Ordines, Genera, Species Cum Caracteribus, Differentiis, Synonymis, Locis, 10th edn. Laurentii Salvii, Stockholm (1758) Google Scholar
  18. Miller, G.A.: The magical number 7, plus or minus 2. Psychol. Rev. 63, 81–97 (1956) CrossRefGoogle Scholar
  19. Milner, R., Tofte, M., Harper, R., MacQueen, D.: The Definition of Standard ML. MIT Press, Cambridge (1997). (Revised) Google Scholar
  20. Olsson, R.: Inductive functional programming using incremental program transformation. Artif. Intell. 1, 55–83 (1995) CrossRefGoogle Scholar
  21. Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the rprop algorithm. In: International Conference on Neural Networks, San Francisco (1993) Google Scholar
  22. Schwefel, H.-P.: Evolution and Optimum Seeking. Wiley, New York (1995) Google Scholar
  23. Selman, B., Kautz, H., Cohen, B.: Local Search Strategies for Satisfiability Testing. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 26 (1996) Google Scholar
  24. Tay, J.C., Ho, N.B.: Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Comput. Ind. Eng. 54(3), 453–473 (2008) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Molde University CollegeMoldeNorway
  2. 2.Østfold University CollegeHaldenNorway

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