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Advancing Dynamic Evolutionary Optimization Using In-Memory Database Technology

  • Julia Jordan
  • Wei Cheng
  • Bernd Scheuermann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10200)

Abstract

This paper reports on IMDEA (In-Memory database Dynamic Evolutionary Algorithm), an approach to dynamic evolutionary optimization exploiting in-memory database (IMDB) technology to expedite the search process subject to change events arising at runtime. The implemented system benefits from optimization knowledge persisted on an IMDB serving as associative memory to better guide the optimizer through changing environments. For this, specific strategies for knowledge processing, extraction and injection are developed and evaluated. Moreover, prediction methods are embedded and empirical studies outline to which extent these methods are able to anticipate forthcoming dynamic change events by evaluating historical records of previous changes and other optimization knowledge managed by the IMDB.

Keywords

Dynamic evolutionary algorithm Associative memory Prediction In-memory databases 

Notes

Acknowledgments

The work for this paper was generously supported by the HPI Future SOC Lab in the scope of the project “Big Data in Bio-inspired Optimization”.

References

  1. 1.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  2. 2.
    Plattner, H.: A Course in in-Memory Data Management: The Inner Mechanics of in-Memory Databases. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  3. 3.
    Kellerer, H., Pferschy, U., Pisinger, D.: Knapsack Problems. Springer, Heidelberg (2004)CrossRefzbMATHGoogle Scholar
  4. 4.
    Nguyen, T.T., Yang, S., Branke, J.: Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol. Comput. 6, 1–24 (2012)CrossRefGoogle Scholar
  5. 5.
    Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Congress on Evolutionary Computation, CEC 1999, pp. 1875–1882 (1999)Google Scholar
  6. 6.
    Yang, S.: Explicit memory schemes for evolutionary algorithms in dynamic environments. In: Yang, S., Ong, Y.S., Jin, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments. SCI, vol. 51, pp. 3–28. Springer, Berlin London (2007)CrossRefGoogle Scholar
  7. 7.
    Grefenstette, J.J., Ramsey, C.L.: Case-based initialization of genetic algorithms. In: Proceedings of the 5th ICGA, pp. 84–91 (1993)Google Scholar
  8. 8.
    Rossi, C., Abderrahim, M., Díaz, J.C.: Tracking moving optima using Kalman-based predictions. Evol. Comput. 16, 1–30 (2008)CrossRefGoogle Scholar
  9. 9.
    Simões, A., Costa, E.: Prediction in evolutionary algorithms for dynamic environments. Soft Comput. 18, 1471–1497 (2014)CrossRefGoogle Scholar
  10. 10.
    Fogel, L., Owens, A., Walsh, M.: Artificial Intelligence Through Simulated Evolution. Wiley, New York (1966)zbMATHGoogle Scholar
  11. 11.
    Cruz, C., Gonzalez, J.R., Pelta, D.A.: Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput. 15, 1427–1448 (2011)CrossRefGoogle Scholar
  12. 12.
    Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms. In: Cattolico, M. (ed.) Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 1201–1208. ACM, New York (2006)Google Scholar
  13. 13.
    Simões, A., Costa, E.: Variable-size memory evolutionary algorithm to deal with dynamic environments. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 617–626. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-71805-5_68Google Scholar
  14. 14.
    Simões, A., Costa, E.: Evolutionary algorithms for dynamic environments: prediction using linear regression and Markov chains. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 306–315. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-87700-4_31CrossRefGoogle Scholar
  15. 15.
    Simões, A., Costa, E.: Prediction in evolutionary algorithms for dynamic environments using Markov chains and nonlinear regression. In: Rothlauf, F. (ed.) Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, pp. 883–890. ACM, New York (2009)Google Scholar
  16. 16.
    Deb, K., Goldberg, D.E.: An investigation of niche and species formation in genetic function optimization. In: Proceedings of the 3rd ICGA, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc., pp. 42–50 (1989)Google Scholar
  17. 17.
    Sareni, B., Krähenbühl, L.: Fitness sharing and niching methods revisited. IEEE Trans. Evol. Comput. 2, 97–106 (1998)CrossRefGoogle Scholar
  18. 18.
    Mahfoud, S.W.: Niching methods for genetic algorithms. Ph.D. thesis, University of Illinois UMI Order No. GAX95-43663 (1995)Google Scholar
  19. 19.
    Ishibuchi, H., Shibata, Y.: Mating scheme for controlling the diversity-convergence balance for multiobjective optimization. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3102, pp. 1259–1271. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-24854-5_121CrossRefGoogle Scholar
  20. 20.
    Yang, S.: Memory-based immigrants for genetic algorithms in dynamic environments. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 1115–1122. ACM, New York (2005)Google Scholar
  21. 21.
    Yang, S.: Associative memory scheme for genetic algorithms in dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 788–799. Springer, Heidelberg (2006). doi: 10.1007/11732242_76CrossRefGoogle Scholar
  22. 22.
    Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. Evol. Comput. 12, 542–561 (2008)CrossRefGoogle Scholar
  23. 23.
    Plattner, H.: A common database approach for OLTP and OLAP using an in-memory column database. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 1–2. ACM, New York (2009)Google Scholar
  24. 24.
    Silvia, P., Frye, R., Berg, B.: SAP HANA - An Introduction. Rheinwerk Verlag, Birmingham (2016)Google Scholar
  25. 25.
    Plattner, H., Leukert, B.: The In-Memory Revolution: How SAP HANA Enables Business of the Future. Springer, Heidelberg (2015)Google Scholar
  26. 26.
    SAP: SAP HANA XS JavaScript Reference: SAP HANA Platform SPS 12, Document Version: 1.0, 11 May 2016Google Scholar
  27. 27.
    Li, C., Yang, S.: A generalized approach to construct benchmark problems for dynamic optimization. In: Li, X., Kirley, M., Zhang, M., Green, D., Ciesielski, V., Abbass, H., Michalewicz, Z., Hendtlass, T., Deb, K., Tan, K.C., Branke, J., Shi, Y. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 391–400. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-89694-4_40CrossRefGoogle Scholar
  28. 28.
    SAP: SAP HANA Predictive Analysis Library (PAL): SAP HANA Platform SPS 11, Document Version: 1.0, 25 November 2015Google Scholar
  29. 29.
    Beasley, J.E.: mknapcb3 (2004). http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files/mknapcb3.txt. Accessed 25 August 2016
  30. 30.
    Klein, M., Greiner, U., Genßler, T., Kuhn, J., Born, M.: Enabling interoperability in the area of multi-brand vehicle configuration. In: Gonçalves, R.J. (ed.) Enterprise Interoperability II, pp. 759–770. Springer, London (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Hochschule KarlsruheUniversity of Applied SciencesKarlsruheGermany
  2. 2.CAS Software AGKarlsruheGermany
  3. 3.SAP Innovation Center NetworkPotsdamGermany

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