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
Bascompte, J., Jordano, P.: Plant-animal mutualistic networks: the architecture of biodiversity. Annu. Rev. Ecol. Evol. Syst. 38(1), 567–593 (2007)
Biere, A., Bennett, A.E.: Three-way interactions between plants, microbes and insects. Funct. Ecol. 27(3), 567–573 (2013)
Bull, L.: Learning Classifier Systems: A Brief Introduction. Applications of Learning Classifier Systems, pp. 1–12. Springer, Berlin (2004)
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)
Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer, Berlin (2013)
Kuncheva, L.I.: Combining Pattern Classifiers. Wiley-Interscience, Chichester (2004)
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
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
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)
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)
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)
Lones, M.A., Tyrrell, A.M.: Modelling biological evolvability: implicit context and variation filtering in enzyme genetic programming. BioSystems 76(13), 229–238 (2004)
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)
Popovici, E., Bucci, A., Wiegand, R.P., De Jong, E.D.: Coevolutionary Principles. Handbook of Natural Computing, pp. 987–1033. Springer, Berlin (2012)
Reynolds, R.G.: An introduction to cultural algorithms. In: Proceedings of the Third Annual Conference on Evolutionary Programming, pp. 131–139 (1994)
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)
Sörensen, K.: Metaheuristics—the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)
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)
Stewart, J.E.: The direction of evolution: the rise of cooperative organization. Biosystems 123, 27–36 (2014)
Turcotte, M.M., Corrin, M.S.C., Johnson, M.T.J.: Adaptive evolution in ecological communities. PLoS Biol. 10(5), e1001332 (2012)
Wang, B., Qiu, Y.L.: Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza 16(5), 299–363 (2006)
Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178(15), 2985–2999 (2008)
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This work was supported by the EPSRC [grant ref. EP/M013677/1].
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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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