Skip to main content
SpringerLink
Log in

Predictive Memetic Algorithm (PMA) for Combinatorial Optimization in Dynamic Environments

  • Conference paper
  • First Online:
Computational Collective Intelligence (ICCCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11056))

Included in the following conference series:

Abstract

A prediction mechanism for Memetic Algorithm is presented in this paper. The Predictive Memetic Algorithm (PMA) uses a nonlinear regression method to estimate the parameters used by the algorithm to obtain good solutions in a dynamic and stochastic environment. The algorithm is applied to nonlinear data sets and performance is compared with genetic and simulated annealing algorithms. When compared with the existing methods, the proposed method generates a relatively small error difference after prediction thereby proving its superior performance. A dynamic stochastic environment is used for experimentation, so as to determine the efficacy of the algorithm on non-stationary problem environments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program, pp. 1–67 (1989)

    Google Scholar 

  2. Cassar, I.R., Titus, N.D.: An improved genetic algorithm for designing optimal temporal patterns of neural stimulation. J. Neural Eng. 14, 1–15 (2017)

    Article  Google Scholar 

  3. Forbes, N.: Imitation of Life: How Biology is Inspiring Computing, 1st edn. MIT Press, Cambridge (2004)

    Google Scholar 

  4. Dawkins, R.: Universal Darwinism. In: Bendall, D.S. (ed.) Evolution from Molecules to Man, pp. 2–16. Cambridge University Press, Cambridge (1983)

    Google Scholar 

  5. Dawkins, R.: Memes: The New Replicators, 2nd edn, pp. 188–300. Oxford University Press, Oxford (1989)

    Google Scholar 

  6. Merz, P., Freisleben, B.: Memetic algorithms for the traveling salesman problem. J. Complex Syst. 13, 297–345 (1997)

    MathSciNet  MATH  Google Scholar 

  7. Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop schedulings. IEEE Trans. Evol. Comput. Jpn. 7, 204–223 (2003)

    Article  Google Scholar 

  8. Tang, J., Lim, M.H., Ong, Y.S.: Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput. 11, 873–888 (2007)

    Article  Google Scholar 

  9. Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans. Evol. Comput. 2, 204–223 (2003)

    Article  Google Scholar 

  10. Alkan, A., Ozcan, E.: Memetic algorithms for timetabling. In: IEEE Proceedings of the 2003 Congress on Evolutionary Computation, vol. 2, pp. 1796–1802 (2003)

    Google Scholar 

  11. Burke, E.K., Newall, J.P.: A multi-stage evolutionary algorithm for the timetable problem. IEEE Trans. Evol. Comput. 3, 1085–1092 (1999)

    Article  Google Scholar 

  12. Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Keijzer, M. (ed.) Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1201–1208. ACM (2006)

    Google Scholar 

  13. AbdAllah, A.M.F.M., Essam, D.L., Sarker, R.A.: Solving dynamic optimisation problem with variable dimensions. In: Dick, G., et al. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 1–12. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13563-2_1

    Chapter  Google Scholar 

  14. Zhou, Z., Ong, Y.S., Lim, M.H.: Memetic algorithm using multi-surrogates for computationally expensive optimization problems. Soft Comput. 11, 957–971 (2007)

    Article  Google Scholar 

  15. Mavrovouniotis, M., Yang, S.: A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Comput. 15, 1405–1425 (2011)

    Article  Google Scholar 

  16. William, H., Krasnogor, N., Smith, J.E.: Recent Advances in Memetic Algorithms. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-32363-5

    Book  MATH  Google Scholar 

  17. Weiss, G.: Timeweaver: a genetic algorithm for identifying predictive patterns in sequences of events. In: Spector, L. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 718–725 (1999)

    Google Scholar 

  18. Fang, K.T., Zhang, J.T.: A new algorithm for calculation of estimates of parameters of nonlinear regression modelings. In: Proceedings of International Conference on Optimization Techniques and Applications, pp. 1–8 (1995)

    Google Scholar 

  19. Rawlings, J.O., Pantula, S.G., Dickey, D.A.: Applied Regression Analysis: A Research Tool. Springer, New York (1998). https://doi.org/10.1007/b98890

    Book  MATH  Google Scholar 

  20. Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., Tsang, E.: Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 832–846. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-70928-2_62

    Chapter  Google Scholar 

  21. Hemert, J.V., Hoyweghen, C.V., Lukshandl, E., Verbeeck, K.: A futurist approach to dynamic environments. In: Branke, J., Back, T. (eds.) Proceedings for Evolutionary Algorithms for Dynamic Optimization Problems at the Genetic and Evolutionary Computation Conference, pp. 1–10 (2001)

    Google Scholar 

  22. Branke, J.: Evolutionary Optimization in Dynamic Environments, vol. 3. Springer, New York (2002). https://doi.org/10.1007/978-1-4615-0911-0

    Book  MATH  Google Scholar 

  23. Weicker, K.: Evolutionary algorithms and dynamic optimization problems. Ph.D. thesis. University of Stuttgart, Germany (2003)

    Google Scholar 

  24. Stroud, P.D.: Kalman-extended genetic algorithm for search in nonstationary environments with noisy fitness evaluations. IEEE Trans. Evol. Comput. 5, 66–77 (2001)

    Article  Google Scholar 

  25. Karaman, A., Uyar, Ş., Eryiğit, G.: The memory indexing evolutionary algorithm for dynamic environments. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 563–573. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-32003-6_59

    Chapter  Google Scholar 

  26. Bosman, P.A.N., La Poutré, H.: Computationally intelligent online dynamic vehicle routing by explicit load prediction in an evolutionary algorithm. In: Runarsson, T.P., Beyer, H.-G., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 312–321. Springer, Heidelberg (2006). https://doi.org/10.1007/11844297_32

    Chapter  Google Scholar 

  27. Simoes, A., Costa, E.: Evaluating predictor’s accuracy in evolutionary algorithms for dynamic environments. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 883–890 (2009)

    Google Scholar 

  28. DeJong, K.: Evolutionary computation: a unified approach. J. Evol. Comput. 164–270 (2006)

    Google Scholar 

  29. Kapanoglua, M., Koca, I.O., Erdogmus, S.: Genetic algorithms in parameter estimation for nonlinear regression models: an experimental approach. J. Stat. Comput. Simul. 77, 851–867 (2007)

    Article  MathSciNet  Google Scholar 

  30. Vollinger, U., Lehmann, E., Rainer, S.: Using memetic algorithms for the solution of technical problems. Int. Sch. Sci. Res. Innov. 3 (2009)

    Google Scholar 

  31. Bu, Z., Zheng, B.: Perspectives in dynamic optimization evolutionary algorithm. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds.) ISICA 2010. LNCS, vol. 6382, pp. 338–348. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16493-4_35

    Chapter  Google Scholar 

  32. Wang, H., Wang, D., Yang, S.: A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput. 13, 763–780 (2008)

    Article  Google Scholar 

  33. KJason, B.: Clever Algorithms: Nature-Inspired Programming Recipes. Lulu Enterprises, Morrisville (2011)

    Google Scholar 

  34. Hart, W.E., Krasnogor, N., Smith, J.E.: Memetic evolutionary algorithms. In: Hart, W.E., Smith, J.E., Krasnogor, N. (eds.) Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing, vol. 166, pp. 3–27. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-32363-5_1

    Chapter  MATH  Google Scholar 

  35. Gen, M., Cheng, R.: Genetic Algorithms and Engineering Optimization. Wiley, Hoboken (2000)

    Google Scholar 

  36. Eiben, A., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Comput. Soc. 124–141 (1999)

    Google Scholar 

  37. Gerdes, I., Klawonn, F., Kruse, R.: Evolutionre Algorithmen. Vieweg Verlag, Wiesbaden (2004)

    Book  Google Scholar 

  38. Schneburg, E., Heinzmann, F., Feddersen, S.: Genetische Algorithmen und Evolutionsstrategien. Addison-Wesley, Boston (1994)

    MATH  Google Scholar 

  39. Weicker, K.: Evolution are Algorithmen, 1st edn. Teubner Verlag, Wiesbaden (2002)

    MATH  Google Scholar 

  40. Seber, G.A.F., Wild, C.J.: Nonlinear Regression. Wiley, Hoboken (2003)

    MATH  Google Scholar 

  41. Hongfeng, W., Dingwei, W., Shengxiang, Y.: A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput. 13, 763–780 (2009)

    Article  Google Scholar 

  42. Weicker, K.: Evolutionary algorithms and dynamic optimization problems. Thesis. University of Stuttgart, Germany (2003)

    Google Scholar 

  43. Simoes, A., Costa, E.: Improving memory’s usage in evolutionary algorithms for changing environments. In: IEEE Congress of Evolutionary Computation, pp. 276–283 (2007)

    Google Scholar 

  44. Smyth, G.K.: Nonlinear regression. Encycl. Environ. 3, 1405–1411 (2002)

    Google Scholar 

  45. Fang, K.T., Zhang, J.T.: A new algorithm for calculation of estimates of parameters of nonlinear regression modellings. Acta Math. Appl. Sin. 16, 366–377 (1993)

    MATH  Google Scholar 

  46. National Institute of Standards and Technology: Nonlinear Regression. StRD (2018). Accessed http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml

Download references

Author information

Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa

    Stephen M. Akandwanaho & Serestina Viriri

Authors
  1. Stephen M. Akandwanaho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Serestina Viriri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Serestina Viriri .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Elias Pimenidis

  3. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Zaheer Khan

  4. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Akandwanaho, S.M., Viriri, S. (2018). Predictive Memetic Algorithm (PMA) for Combinatorial Optimization in Dynamic Environments. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11056. Springer, Cham. https://doi.org/10.1007/978-3-319-98446-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-98446-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98445-2

  • Online ISBN: 978-3-319-98446-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation