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Evolving Ensembles: What Can We Learn from Biological Mutualisms?

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Information Processing in Cells and Tissues (IPCAT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9303))

Abstract

Ensembles are groups of classifiers which cooperate in order to reach a decision. Conventionally, the members of an ensemble are trained sequentially, and typically independently, and are not brought together until the final stages of ensemble generation. In this paper, we discuss the potential benefits of training classifiers together, so that they learn to interact at an early stage of their development. As a potential mechanism for achieving this, we consider the biological concept of mutualism, whereby cooperation emerges over the course of biological evolution. We also discuss potential mechanisms for implementing this approach within an evolutionary algorithm context.

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References

  1. Bascompte, J., Jordano, P.: Plant-animal mutualistic networks: the architecture of biodiversity. Annu. Rev. Ecol. Evol. Syst. 38(1), 567–593 (2007)

    Article  Google Scholar 

  2. Biere, A., Bennett, A.E.: Three-way interactions between plants, microbes and insects. Funct. Ecol. 27(3), 567–573 (2013)

    Article  Google Scholar 

  3. Bull, L.: Learning Classifier Systems: A Brief Introduction. Applications of Learning Classifier Systems, pp. 1–12. Springer, Berlin (2004)

    Google Scholar 

  4. Fuente, L.A., Lones, M.A., Turner, A.P., Stepney, S., Caves, L.S., Tyrrell, A.M.: Computational models of signalling networks for non-linear control. BioSystems 112(2), 122–130 (2013)

    Article  Google Scholar 

  5. Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer, Berlin (2013)

    Book  MATH  Google Scholar 

  6. Kuncheva, L.I.: Combining Pattern Classifiers. Wiley-Interscience, Chichester (2004)

    Book  MATH  Google Scholar 

  7. Lacy, S., Lones, M.A., Smith, S.L.: A comparison of evolved linear and non-linear ensemble vote aggregators. In: Proceedings of 2015 Congress on Evolutionary Computation, CEC 2015. IEEE Press, May 2015

    Google Scholar 

  8. Lacy, S., Lones, M.A., Smith, S.L.: Forming classifier ensembles with multimodal evolutionary algorithms. In: Proceeding of 2015 Congress on Evolutionary Computation, CEC 2015. IEEE Press, May 2015

    Google Scholar 

  9. Lones, M.A., Smith, S.L., Alty, J.E., Lacy, S.E., Possin, K.L., Jamieson, D.R.S., Tyrrell, A.M.: Evolving classifiers to recognize the movement characteristics of parkinson’s disease patients. IEEE Trans. Evol. Comput. 18(4), 559–576 (2014)

    Article  Google Scholar 

  10. Lones, M., Alty, J.E., Lacy, S.E., Jamieson, D., Possin, K.L., Schuff, N., Smith, S.L., et al.: Evolving classifiers to inform clinical assessment of parkinson’s disease. In: 2013 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), pp. 76–82. IEEE (2013)

    Google Scholar 

  11. Lones, M.A., Turner, A.P., Fuente, L.A., Stepney, S., Caves, L.S., Tyrrell, A.M.: Biochemical connectionism. Nat. Comput. 12(4), 453–472 (2013)

    Article  MathSciNet  Google Scholar 

  12. Lones, M.A., Tyrrell, A.M.: Modelling biological evolvability: implicit context and variation filtering in enzyme genetic programming. BioSystems 76(13), 229–238 (2004)

    Article  Google Scholar 

  13. Lones, M.A., Tyrrell, A.M.: A co-evolutionary framework for regulatory motif discovery. In: IEEE Conference on Evolutionary Computation, CEC 2007, pp. 3894–3901. IEEE (2007)

    Google Scholar 

  14. Popovici, E., Bucci, A., Wiegand, R.P., De Jong, E.D.: Coevolutionary Principles. Handbook of Natural Computing, pp. 987–1033. Springer, Berlin (2012)

    Google Scholar 

  15. Reynolds, R.G.: An introduction to cultural algorithms. In: Proceedings of the Third Annual Conference on Evolutionary Programming, pp. 131–139 (1994)

    Google Scholar 

  16. Santos, F.C., Pinheiro, F.L., Lenaerts, T., Pacheco, J.M.: The role of diversity in the evolution of cooperation. J. Theor. Biol. 299, 88–96 (2012)

    Article  MathSciNet  Google Scholar 

  17. Sörensen, K.: Metaheuristics—the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)

    Article  MathSciNet  Google Scholar 

  18. Spector, L., Luke, S.: Cultural transmission of information in genetic programming. In: Proceedings of the First Annual Conference on Genetic Programming, pp. 209–214. MIT Press (1996)

    Google Scholar 

  19. Stewart, J.E.: The direction of evolution: the rise of cooperative organization. Biosystems 123, 27–36 (2014)

    Article  Google Scholar 

  20. Turcotte, M.M., Corrin, M.S.C., Johnson, M.T.J.: Adaptive evolution in ecological communities. PLoS Biol. 10(5), e1001332 (2012)

    Article  Google Scholar 

  21. Wang, B., Qiu, Y.L.: Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza 16(5), 299–363 (2006)

    Article  Google Scholar 

  22. Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178(15), 2985–2999 (2008)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported by the EPSRC [grant ref. EP/M013677/1].

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Authors and Affiliations

  1. School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK

    Michael A. Lones

  2. Department of Electronics, University of York, York, YO10 5DD, UK

    Stuart E. Lacy & Stephen L. Smith

Authors
  1. Michael A. Lones
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  2. Stuart E. Lacy
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  3. Stephen L. Smith
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Corresponding author

Correspondence to Michael A. Lones .

Editor information

Editors and Affiliations

  1. Heriot-Watt University, Edinburgh, United Kingdom

    Michael Lones

  2. University of York, York, United Kingdom

    Andy Tyrrell

  3. University of York, York, United Kingdom

    Stephen Smith

  4. Natural Selection, Inc., San Diego, California, USA

    Gary Fogel

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Lones, M.A., Lacy, S.E., Smith, S.L. (2015). Evolving Ensembles: What Can We Learn from Biological Mutualisms?. In: Lones, M., Tyrrell, A., Smith, S., Fogel, G. (eds) Information Processing in Cells and Tissues. IPCAT 2015. Lecture Notes in Computer Science(), vol 9303. Springer, Cham. https://doi.org/10.1007/978-3-319-23108-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-23108-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23107-5

  • Online ISBN: 978-3-319-23108-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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