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Optimisation and fitness modelling of bio-control in mushroom farming using a Markov network eda

Published: 12 July 2008 Publication History

Abstract

We explore the application of an Estimation of Distribution Algorithm which uses a Markov Network to the problem of bio-control in mushroom farming. This falls into the category of .bang-bang control. problems and was previously used as an application for genetic algorithms with modified crossover operators. The EDA yields a small improvement in the solutions that are evolved. Moreover, the probabilistic models constructed closely match identifiable features in the underlying dynamics of the problem. We conclude that this is a useful by-product of the probabilistic modelling which can be further exploited.

References

[1]
A.Fenton, R.L.Gwynn,A.Gupta, R.Norman, J.P.Fairbairn, and P.J.Hudson. Optimal application strategies for entomopathogenic nematodes: integrating theoretical and empirical approaches. Journal of Applied Ecology 2002 39,481--492.
[2]
P. M. Godley, D. E. Cairns, and J. Cowie. Directed intervention crossover applied to bio-control scheduling. In IEEE CEC 2007: Proceedings of the IEEE Congress On Evolutionary Computation, 2007.
[3]
P. M. Godley, D. E. Cairns, and J. Cowie. Maximising the efficiency of bio-control application utilising genetic algorithms. In EFITA / WCCA 2007: Proceedings of the 6th Biennial Conference of European Federation of IT in Agriculture, Glasgow, Scotland, UK, 2007. Glasgow Caledonian University.
[4]
P. M. Godley, J. Cowie and D. E. Cairns, Novel Genetic Algorithm Approaches for Time-Series Problems, Doctoral Symposium on Engineering Stochastic Local Search Algorithms pp. 47--51, IRIDIA, 2007 ISSN: 1781--3794
[5]
R. Santana. Probabilistic Modeling Based on Undirected Graphs in Estimation Distribution Algorithms. Ph.D. dissertation, Institute of Cybernetics, Mathematics and Physics, Havana, Cuba, 2003.
[6]
R. Santana. Estimation of Distribution Algorithms with Kikuchi Approximation. Evolutonary Computation, vol. 13, no. 1, pp. 67--98, 2005.
[7]
S. Shakya. DEUM: A Framework for an Estimation of Distribution Algorithm Based on Markov Random Fields. Ph.D. dissertation, The Robert Gordon University, Aberdeen, UK, April 2006.
[8]
S. Shakya, John McCall. Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields. International Journal of Automation and Computing.04(3), July 2007, 262--272.

Cited By

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  • (2016)Mining Markov Network Surrogates for Value-Added OptimisationProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2931711(1267-1274)Online publication date: 20-Jul-2016
  • (2013)An application of a GA with Markov network surrogate to feature selectionInternational Journal of Systems Science10.1080/00207721.2012.68444944:11(2039-2056)Online publication date: 1-Nov-2013
  • (2012)The Markov Network Fitness ModelMarkov Networks in Evolutionary Computation10.1007/978-3-642-28900-2_8(125-140)Online publication date: 2012
  • Show More Cited By

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cover image ACM Conferences
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
July 2008
1814 pages
ISBN:9781605581309
DOI:10.1145/1389095
  • Conference Chair:
  • Conor Ryan,
  • Editor:
  • Maarten Keijzer
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2008

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  1. estimation of distribution algorithms
  2. modelling
  3. real-world applications

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Cited By

View all
  • (2016)Mining Markov Network Surrogates for Value-Added OptimisationProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2931711(1267-1274)Online publication date: 20-Jul-2016
  • (2013)An application of a GA with Markov network surrogate to feature selectionInternational Journal of Systems Science10.1080/00207721.2012.68444944:11(2039-2056)Online publication date: 1-Nov-2013
  • (2012)The Markov Network Fitness ModelMarkov Networks in Evolutionary Computation10.1007/978-3-642-28900-2_8(125-140)Online publication date: 2012
  • (2012)Applications of Distribution Estimation Using Markov Network Modelling (DEUM)Markov Networks in Evolutionary Computation10.1007/978-3-642-28900-2_12(193-207)Online publication date: 2012
  • (2010)DEUM – A Fully Multivariate EDA Based on Markov NetworksExploitation of Linkage Learning in Evolutionary Algorithms10.1007/978-3-642-12834-9_4(71-93)Online publication date: 2010
  • (2009)A fully multivariate DEUM algorithmProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689661(479-486)Online publication date: 18-May-2009
  • (2009)A fully multivariate DEUM algorithm2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4982984(479-486)Online publication date: May-2009

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