Skip to main content
SpringerLink
Log in

Sub-structural Niching in Non-stationary Environments

  • Conference paper
AI 2004: Advances in Artificial Intelligence (AI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3339))

Included in the following conference series:

Abstract

Niching enables a genetic algorithm (GA) to maintain diversity in a population. It is particularly useful when the problem has multiple optima where the aim is to find all or as many as possible of these optima. When the fitness landscape of a problem changes overtime, the problem is called non–stationary, dynamic or time–variant problem. In these problems, niching can maintain useful solutions to respond quickly, reliably and accurately to a change in the environment. In this paper, we present a niching method that works on the problem substructures rather than the whole solution, therefore it has less space complexity than previously known niching mechanisms. We show that the method is responding accurately when environmental changes occur.

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 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abbass, H.A., Sastry, K., Goldberg, D.: Oiling the wheels of change: The role of adaptive automatic problem decomposition in non-stationary environments. Technical Report Illigal TR-2004029, University of Illinois, Urbana–Champaign (2004)

    Google Scholar 

  2. Ackley, D.H.: A connectionist machine for genetic hill climbing. Kluwer Academic publishers, Dordrecht (1987)

    Google Scholar 

  3. Baluja, S.: Population–based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, Carnegie Mellon University (1994)

    Google Scholar 

  4. Bierwirth, C., Mattfeld, D.C.: Production scheduling and rescheduling with genetic algorithms. Evolutionary Computation 7(1), 1–18 (1999)

    Article  Google Scholar 

  5. Bosman, P., Thierens, D.: Linkage information processing in distribution estimation algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 60–67 (1999)

    Google Scholar 

  6. Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Boston (2001)

    Google Scholar 

  7. Y.-p. Chen. Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL (Also IlliGAL Report No. 2004018) (2004)

    Google Scholar 

  8. Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory (1990)

    Google Scholar 

  9. Collard, P., Escazut, C., Gaspar, E.: An evolutionnary approach for time dependant optimization. International Journal on Artificial Intelligence Tools 6(4), 665–695 (1997)

    Article  Google Scholar 

  10. Dasgupta, D.: Incorporating redundancy and gene activation mechanisms in genetic search. In: Chambers, L. (ed.) Practical Handbook of Genetic Algorithms, pp. 303–316. CRC Press, Boca Raton (1995)

    Google Scholar 

  11. Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Foundations of Genetic Algorithms, pp. 93–108. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  12. Ghosh, A., Tstutsui, S., Tanaka, H.: Function optimisation in nonstationary environment using steady state genetic algorithms with aging of individuals. In: IEEE International Conference on Evolutionary Computation, pp. 666–671. IEEE Computer Society Press, Los Alamitos (1998)

    Google Scholar 

  13. Goldberg, D.E., Deb, K., Horn, J.: Massive multimodality, deception, and genetic algorithms. In: Proceedings of parallel problem solving from nature II, pp. 37–46. Elsevier Science Publishers, Amsterdam (1992)

    Google Scholar 

  14. Goldberg, D.E.: The design of innovation: lessons from and for competent genetic algorithms. Kluwer Academic Publishers, Massachusetts (2002)

    MATH  Google Scholar 

  15. Goldberg, D.E., Deb, K., Kargupta, H., Harik, G.: Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In: Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California, pp. 56–530. Morgan Kauffman Publishers, San Francisco (1993)

    Google Scholar 

  16. Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: motivation, analysis, and first results. Complex Systems 3(5), 493–530 (1989)

    MATH  MathSciNet  Google Scholar 

  17. Goldberg, D.E., Smith, R.E.: Nonstationary function optimisation using genetic algorithms with dominance and diploidy. In: Second International Conference on Genetic Algorithms, pp. 59–68. Lawrence Erlbaum Associates, Mahwah (1987)

    Google Scholar 

  18. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Proceedings of Parallel Problem Solving from Nature, pp. 137–144. Elsevier Science Publisher, Amsterdam (1992)

    Google Scholar 

  19. Harik, G.: Linkage Learning via Probabilistic Modeling in the ECGA. PhD thesis, University of Illinois at Urbana–Champaign (1999)

    Google Scholar 

  20. Heckendorn, R.B., Wright, A.H.: Efficient linkage discovery by limited probing. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 1003–1014. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  21. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  22. Kargupta, H.: The gene expression messy genetic algorithm. In: Proceedings of the IEEE International Conference on Evolutionary Computation, Piscataway, NJ, pp. 814–819. IEEE Service Centre, Los Alamitos (1996)

    Chapter  Google Scholar 

  23. Lin, S.C., Goodman, E.D., Punch, W.F.: A genetic algorithm approach to dynamic job shop scheduling problems. In: Seventh International Conference on Genetic Algorithms, pp. 139–148. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  24. Mahfoud, S.W.: Bayesian. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL (Also IlliGAL Report No. 95001) (1995)

    Google Scholar 

  25. Mori, N., Imanishia, S., Kita, H., Nishikawa, Y.: Adaptation to changing environments by means of the memory based thermodynamical genetic algorithms. In: Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 299–306. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  26. Mori, N., Kita, H., Nishikawa, Y.: Adaptation to changing environments by means of the thermodynamical genetic algorithms. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 513–522. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  27. Munetomo, M., Goldberg, D.: Linkage identification by non-monotonicity detection for overlapping functions. Evolutionary Computation 7(4), 377–398 (1999)

    Article  Google Scholar 

  28. Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: Linkage learning, estimation distribution, and Bayesian networks. Evolutionary Computation 8(3), 314–341 (2000) (Also IlliGAL Report No. 98013)

    Article  Google Scholar 

  29. Pelikan, M., Mühlenbein, H.: The bivariate marginal distribution algorithm. In: Roy, R., Furuhashi, T., Chawdhry, P.K. (eds.) Advances in Soft Computing - Engineering Design and Manufacturing, London, pp. 521–535. Springer-Verlag, Heidelberg (1999)

    Google Scholar 

  30. Rissanen, J.J.: Modelling by shortest data description. Automatica 14, 465–471 (1978)

    Article  MATH  Google Scholar 

  31. Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27(3), 379–423 (1948)

    MATH  MathSciNet  Google Scholar 

  32. Thierens, D., Goldberg, D.E.: Mixing in genetic algorithms. In: Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA 1993), San Mateo, CA, pp. 38–45. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  33. Vavak, F., Jukes, K., Fogarty, T.C.: Learning the local search range for genetic optimisation in nonstationary environments. In: IEEE International Conference on Evolutionary Computation, pp. 355–360. IEEE Publishing, Los Alamitos (1997)

    Google Scholar 

  34. Yu, T.-L., Goldberg, D.E., Yassine, A., Chen, Y.-P.: A genetic algorithm design inspired by organizational theory: Pilot study of a dependency structure matrix driven genetic algorithm. Artificial Neural Networks in Engineering, 327–332 (2003) (Also IlliGAL Report No. 2003007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Illinois Genetic Algorithms Laboratory, University of Illinois, 117, Transportation Building, 104, S. Mathews Av., Urbana, IL, 61801, USA

    Kumara Sastry & David Goldberg

  2. Artificial Life and Adaptive Robotics Laboratory (ALAR), School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT, 2600, Australia

    Hussein A. Abbass

Authors
  1. Kumara Sastry
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hussein A. Abbass
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Goldberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Monash University, VIC 3800, Australia

    Geoffrey I. Webb

  2. Science, Engineering and Technology Portfolio, Royal Melbourne Institute of Technology, VIC 3001, Melbourne, Australia

    Xinghuo Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sastry, K., Abbass, H.A., Goldberg, D. (2004). Sub-structural Niching in Non-stationary Environments. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_75

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30549-1_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24059-4

  • Online ISBN: 978-3-540-30549-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation