Skip to main content
SpringerLink
Log in
Book cover

Markov Networks in Evolutionary Computation pp 193–207Cite as

Applications of Distribution Estimation Using Markov Network Modelling (DEUM)

  • Chapter

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 14))

Abstract

In recent years, Markov Network EDAs have begun to find application to a range of important scientific and industrial problems. In this chapter we focus on several applications of Markov Network EDAs classified under the DEUM framework which estimates the overall distribution of fitness from a bitstring population. In Section 1 we briefly review the main features of the DEUM framework and highlight the principal features that havemotivated the selection of applications. Sections 2 - 5 describe four separate applications: chemotherapy optimisation; dynamic pricing; agricultural biocontrol; and case-based feature selection. In Section 6 we summarise the lessons learned from these applications. These include: comparisons with other techniques such as GA and Bayesian Network EDAs; trade-offs between modelling cost and reduction in search effort; and the use of MN models for surrogate evaluation.We also present guidelines for further applications and future research.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, Pittsburgh, PA (1994)

    Google Scholar 

  2. Bichler, M., Kalagnanam, J., Katircioglu, K., King, A.J., Lawrence, R.D., Lee, H.S., Lin, G.Y., Lu, Y.: Applications of flexible pricing in business-to-business electronic commerce. IBM Systems Journal 41(2), 287–302 (2002)

    Article  Google Scholar 

  3. Brownlee, A.E.I.: Multivariate Markov Networks for Fitness Modelling in an Estimation of Distribution Algorithm. PhD thesis, The Robert Gordon University, Aberdeen, UK (May 2009)

    Google Scholar 

  4. Brownlee, A., Pelikan, M., McCall, J., Petrovski, A.: An Application of a Multivariate Estimation of Distribution Algorithm to Cancer Chemotherapy. In: Proc. ACM GECCO 2008, pp. 463–464 (2008)

    Google Scholar 

  5. Brownlee, A., Wu, Y., McCall, J., Godley, P., Cairns, D., Cowie, J.: Optimisation and Fitness Modelling of Bio-control in Mushroom Farming using a Markov Network EDA. In: Proc. ACM GECCO 2008, pp. 465–466 (2008)

    Google Scholar 

  6. Brownlee, A., McCall, J., Shakya, S., Zhang, Q.: Structure Learning and Optimisation in a Markov-network based Estimation of Distribution Algorithm. In: Proc. IEEE CEC 2009, pp. 447–454 (2009)

    Google Scholar 

  7. Brownlee, A., Regnier-Coudert, O., McCall, J., Massie, S.: Using a Markov network as a surrogate fitness function in a genetic algorithm. In: Proc. IEEE CEC 2010, pp. 4525–4532 (2010)

    Google Scholar 

  8. Das, S.: Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection. In: Proc. ICML 2001, pp. 74–81. Morgan Kaufmann Publishers Inc. (2001)

    Google Scholar 

  9. Fenton, A., Gwynn, R.L., Gupta, A., Norman, R., Fairbairn, J.P., Hudson, P.J.: Optimal application strategies for entomopathogenic nematodes: integrating theoretical and empirical approaches. Journal of Applied Ecology 39, 481–492 (2002)

    Article  Google Scholar 

  10. Godley, P.M., Cairns, D.E., Cowie, J.: Directed intervention crossover applied to bio-control scheduling. In: Proc. IEEE CEC 2007, pp. 638–645 (2007)

    Google Scholar 

  11. Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley (1989)

    Google Scholar 

  12. John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Proc. ICML 1994, pp. 121–129. Morgan Kaufmann Publishers Inc. (1994)

    Google Scholar 

  13. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  14. Martin, R.B., Teo, K.L.: Optimal Control of Drug Administration in Cancer Chemotherapy. World Scientific, Singapore (1994)

    MATH  Google Scholar 

  15. McCall, J., Petrovski, A., Shakya, S.: Evolutionary Algorithms for Cancer Chemotherapy Optimization. In: Fogel, G., Corne, D., Pan, Y. (eds.) Computational Intelligence in Bioinformatics, ch. 12, pp. 265–296. Wiley Interscience (2008)

    Google Scholar 

  16. Mühlenbein, H., Paaß, G.: From Recombination of Genes to the Estimation of Distributions I. Binary Parameters. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 178–187. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  17. Narahari, Y., Raju, C.V., Ravikumar, K., Shah, S.: Dynamic pricing models for electronic business. Sadhana 30(part 2,3), 231–256 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  18. Oliveira, F.S.: A constraint logic programming algorithm for modeling dynamic pricing. Informs Journal of Computing 30, 69–77 (2007)

    Google Scholar 

  19. Owusu, G., Dorne, R., Voudouris, C., Lesaint, D.: Dynamic planner: A decision support tool for resource planning, applications and innovations in intelligent systems. In: Proc. of ES 2002, pp. 19–31 (2002)

    Google Scholar 

  20. Owusu, G., Voudouris, C., Kern, M., Garyfalos, A., Anim-Ansah, G., Virginas, B.: On optimising resource planning in BT with FOS. In: Proc. International Conference on Service Systems and Service Management, pp. 541–546 (2006)

    Google Scholar 

  21. Parsopoulos, K., Vrahatis, M.: Particle swarm optimization method for constrained optimization problems. In: Intelligent Technologies - Theory and Application: New Trends in Intelligent Technologies. Frontiers in Artificial Intelligence and Applications, vol. 76, pp. 214–220 (2002)

    Google Scholar 

  22. Pelikan, M.: Hierarchical Bayesian Optimization Algorithms. Springer (2005)

    Google Scholar 

  23. Petrovski, A.: An Application of Genetic Algorithms to Chemotherapy Treatment. PhD Thesis, The Robert Gordon University, Aberdeen (1999)

    Google Scholar 

  24. Petrovski, A., McCall, J.: Computational Optimization of Cancer Chemotherapies using Genetic Algorithms. In: John, R., Birkenhead, R. (eds.) Soft Computing Techniques and Applications, pp. 117–122. Physica-Verlag, Heidelberg (2000)

    Google Scholar 

  25. Petrovski, A., McCall, J.A.W.: Multi-objective Optimisation of Cancer Chemotherapy Using Evolutionary Algorithms. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 531–545. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  26. Petrovski, A., Shakya, S., McCall, J.: Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms. In: Proc. ACM GECCO 2006, pp. 413–418 (2006)

    Google Scholar 

  27. Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)

    Article  Google Scholar 

  28. Shakya, S.: DEUM: A Framework for an Estimation of Distribution Algorithm based on Markov Random Fields. PhD thesis, The Robert Gordon University, Aberdeen, UK (April 2006)

    Google Scholar 

  29. Shakya, S., McCall, J., Brown, D.: Using a Markov Network Model in a Univariate EDA: An Empirical Cost-Benefit Analysis. In: Proc. ACM GECCO, pp. 727–734 (2005)

    Google Scholar 

  30. Shakya, S., Oliveira, F., Owusu, G.: An application of EDA and GA to dynamic pricing. In: Proc. ACM GECCO 2007, pp. 585–592 (2007)

    Google Scholar 

  31. Shakya, S., Oliveira, F., Owusu, G.: Analysing the Effect of Demand Uncertainty in Dynamic Pricing with EAs. In: Bramer, M., Coenen, F., Petridis, M. (eds.) Proc. AI 2008, pp. 77–90. Springer (2009)

    Google Scholar 

  32. Voudouris, C., Owusu, G., Dorne, R., McCormick, A.: FOS: An advanced planning and scheduling suite for service operations. In: Proc. International Conference on Service Systems and Service Management, pp. 1138–1143 (2006)

    Google Scholar 

  33. Wheldon, T.E.: Mathematical Models in Cancer Research. Adam Hilger, Bristol (1988)

    MATH  Google Scholar 

  34. Wiratunga, N., Koychev, I., Massie, S.: Feature Selection and Generalisation for Retrieval of Textual Cases. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 806–820. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  35. Wu, Y., McCall, J., Godley, P., Brownlee, A., Cairns, D., Cowie, J.: Bio-control in mushroom farming using a Markov Network EDA. In: Proc. 2008 IEEE CEC 2008, pp. 2996–3001 (2008)

    Google Scholar 

  36. Yang, Y., Pedersen, J.: A Comparative Study on Feature Selection in Text Categorization. In: Proc. ICML 1997, pp. 412–420. Morgan Kaufmann Publishers Inc. (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Robert Gordon University, Aberdeen, UK

    John McCall

  2. Loughborough University, Loughborough, UK

    Alexander Brownlee

  3. Business Modelling and Operational Transformation Practice, BT Innovate & Design, Ipswich, UK

    Siddhartha Shakya

Authors
  1. John McCall
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Brownlee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siddhartha Shakya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John McCall .

Editor information

Editors and Affiliations

  1. Transformation Practice, BT Innovate & Design, Business Modelling and Operational, Cauldwell Hall Road 172, Ipswich, IP4 5DB, United Kingdom

    Siddhartha Shakya

  2. Intelligent Systems Group, Faculty of Informatics, University of the Basque Country, Boadilla del Monte, San Sebastian, Spain

    Roberto Santana

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Berlin Heidelberg

About this chapter

Cite this chapter

McCall, J., Brownlee, A., Shakya, S. (2012). Applications of Distribution Estimation Using Markov Network Modelling (DEUM). In: Shakya, S., Santana, R. (eds) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28900-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28900-2_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28899-9

  • Online ISBN: 978-3-642-28900-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation