Coevolution in Cartesian Genetic Programming

1. Dolin, B., Bennett III, F.H., Reiffel, G.: Co-evolving an effective fitness sample: Experiments in symbolic regression and distributed robot control. In: Proc. of the 2002 ACM Symp. on Applied Computing, pp. 553–559. ACM, New York (2002)

   Chapter  Google Scholar 

2. Dolinsky, J.U., Jenkinson, I.D., Colquhoun, G.J.: Aplication of genetic programming to the calibration of industrial robots. Computers in Industry 58(3), 255–264 (2007)

   Article  Google Scholar 

3. Gagné, C., Parizeau, M.: Co-evolution of nearest neighbor classifiers. International Journal of Pattern Recognition and Artificial Inteligence 21(5), 921–946 (2007)

   Article  Google Scholar 

4. Harrison, M.L., Foster, J.A.: Co-evolving Faults to Improve the Fault Tolerance of Sorting Networks. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 57–66. Springer, Heidelberg (2004)

   Chapter  Google Scholar 

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

   Article  Google Scholar 

6. Imamura, K., Foster, J.A., Krings, A.W.: The Test Vector Problem and Limitations to Evolving Digital Circuits. In: Proc. of the 2nd NASA/DoD Workshop on Evolvable Hardware, pp. 75–79. IEEE Computer Society (2000)

   Google Scholar 

7. Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing Journal 9(1), 3–12 (2005)

   Article  Google Scholar 

8. Mendes, R.R.F., de Voznika, F.B., Freitas, A.A., Nievola, J.C.: Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 314–325. Springer, Heidelberg (2001)

   Chapter  Google Scholar 

9. Miller, J.F., Thomson, P.: Aspects of Digital Evolution: Geometry and Learning. In: Sipper, M., Mange, D., Pérez-Uribe, A. (eds.) ICES 1998. LNCS, vol. 1478, pp. 25–35. Springer, Heidelberg (1998)

   Chapter  Google Scholar 

10. Miller, J.F.: Cartesian Genetic Programming. Springer, Heidelberg (2011)

    Book  MATH  Google Scholar 

11. Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

12. Pagie, L., Hogeweg, P.: Evolutionary consequences of coevolving targets. Evolutionary Computation 5(4), 401–418 (1997)

    Article  Google Scholar 

13. Schmidt, M., Lipson, H.: Co-evolving fitness predictors for accelerating and reducing evaluations. In: Genetic Prog. Theory and Practice IV, vol. 5, pp. 113–130 (2006)

    Google Scholar 

14. Schmidt, M.D., Lipson, H.: Coevolution of Fitness Predictors. IEEE Transactions on Evolutionary Computation 12(6), 736–749 (2008)

    Article  Google Scholar 

15. Vasicek, Z., Sekanina, L.: Formal verification of candidate solutions for post-synthesis evolutionary optimization in evolvable hardware. Genetic Programming and Evolvable Machines 12(3), 305–327 (2011)

    Google Scholar 

16. Vladislavleva, E.: Symbolic Regression: Toy Problems for Symbolic Regression (2009-2010), http://www.vanillamodeling.com/toyproblems.html

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