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New Pathways in Coevolutionary Computation

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Genetic Programming Theory and Practice XVII

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

The simultaneous evolution of two or more species with coupled fitness—coevolution—has been put to good use in the field of evolutionary computation. Herein, we present two new forms of coevolutionary algorithms, which we have recently designed and applied with success. OMNIREP is a cooperative coevolutionary algorithm that discovers both a representation and an encoding for solving a particular problem of interest. SAFE is a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions needed to measure solution quality during evolution.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Coevolution.

  2. 2.

    https://en.wikipedia.org/wiki/Symbiosis.

References

  1. Cheng, T., Chen, M., Fleming, P.J., Yang, Z., Gan, S.: A novel hybrid teaching learning based multi-objective particle swarm optimization. Neurocomputing 222, 11–25 (2017)

    Article  Google Scholar 

  2. Dick, G., Yao, X.: Model representation and cooperative coevolution for finite-state machine evolution. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2700–2707. IEEE, Piscataway, NJ (2014)

    Google Scholar 

  3. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer-Verlag, Berlin (2003)

    Book  Google Scholar 

  4. Han, F., Sun, Y.W.T., Ling, Q.H.: An improved multiobjective quantum-behaved particle swarm optimization based on double search strategy and circular transposon mechanism. Complexity 2018 (2018)

    Google Scholar 

  5. Hillis, W.: Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D: Nonlinear Phenomena 42(1), 228–234 (1990)

    Article  Google Scholar 

  6. Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10(5), 477–506 (2006)

    Article  Google Scholar 

  7. Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: Proceedings of the Eleventh International Conference on Artificial Life (ALIFE). MIT Press, Cambridge, MA (2008)

    Google Scholar 

  8. Pena-Reyes, C.A., Sipper, M.: Fuzzy CoCo: A cooperative-coevolutionary approach to fuzzy modeling. IEEE Transactions on Fuzzy Systems 9(5), 727–737 (2001)

    Article  Google Scholar 

  9. Potter, M.A., De Jong, K.A.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29 (2000)

    Article  Google Scholar 

  10. Sipper, M., Moore, J.H.: OMNIREP: originating meaning by coevolving encodings and representations. Memetic Computing (2019)

    Google Scholar 

  11. Sipper, M., Moore, J.H., Urbanowicz, R.J.: Solution and fitness evolution (SAFE): A study of multiobjective problems. In: Proceedings of 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE (2019)

    Google Scholar 

  12. Sipper, M., Moore, J.H., Urbanowicz, R.J.: Solution and fitness evolution (SAFE): Coevolving solutions and their objective functions. In: L. Sekanina, T. Hu, N. Lourenço, H. Richter, P. García-Sánchez (eds.) Genetic Programming, pp. 146–161. Springer International Publishing, Cham (2019)

    Chapter  Google Scholar 

  13. Sipper, M., Urbanowicz, R.J., Moore, J.H.: To know the objective is not (necessarily) to know the objective function. BioData Mining 11(1) (2018)

    Google Scholar 

  14. Urbanowicz, R.J., Kiralis, J., Sinnott-Armstrong, N.A., Heberling, T., Fisher, J.M., Moore, J.H.: GAMETES: A fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData Mining 5(1), 16 (2012)

    Article  Google Scholar 

  15. Zaritsky, A., Sipper, M.: Coevolving solutions to the shortest common superstring problem. Biosystems 76(1), 209–216 (2004)

    Article  Google Scholar 

  16. Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 1(1), 32–49 (2011)

    Article  Google Scholar 

  17. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173–195 (2000)

    Article  Google Scholar 

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Acknowledgement

This work was supported by National Institutes of Health (USA) grants AI116794, LM010098, and LM012601.

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Sipper, M., Moore, J.H., Urbanowicz, R.J. (2020). New Pathways in Coevolutionary Computation. In: Banzhaf, W., Goodman, E., Sheneman, L., Trujillo, L., Worzel, B. (eds) Genetic Programming Theory and Practice XVII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-39958-0_15

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  • DOI: https://doi.org/10.1007/978-3-030-39958-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39957-3

  • Online ISBN: 978-3-030-39958-0

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