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Part of the book series: Studies in Computational Intelligence ((SCI,volume 267))

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Abstract

This book presents and discusses the interdisciplinary scientific field between deterministic chaos and evolutionary techniques. As demonstrated in the previous chapters, this research is very promising. In this chapter, we would like to offer a few exciting and realistic ideas and opinions for possible future directions of research and development on chaos and evolutionary techniques.

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References

  1. Oplatkova, Z.: Metaevolution — synthesis of evolutionary algorithms by means of symbolic regression, Ph.D. thesis, TBU Zlin (2007)

    Google Scholar 

  2. Hany, H.A., Tao, Y.: Fingerprint registration using genetic algorithms. In: 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology (ASSET 2000), p. 148 (2000)

    Google Scholar 

  3. Stützle, T., Hoos, H.: The Max-Min Ant System and Local Search for the Travelling Salesman Problem. In: Bäck, T., Michalewicz, Z., Yao, X. (eds.) IEEE International Conference on Evolutionary Computation, Piscataway, pp. 309–314. IEEE Press, Los Alamitos (1997)

    Google Scholar 

  4. Gambardella, L.M., Dorigo, M.: Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem. In: Prieditis, A., Russell, S. (eds.) Proceedings of ML 1995, Twelfth International Conference on Machine Learning, Tahoe City, CA, pp. 252–260. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  5. Bartels, R.A., Murnane, M.M., Kapteyn, H.C., Christov, I., Rabitz, H.: Learning from learning algorithms: Application to attosecond dynamics of high-harmonic generation. Phys. Rev. A 70, 043404 (2004)

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Zelinka, I., Celikovsky, S. (2010). Frontiers. In: Zelinka, I., Celikovsky, S., Richter, H., Chen, G. (eds) Evolutionary Algorithms and Chaotic Systems. Studies in Computational Intelligence, vol 267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10707-8_16

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  • DOI: https://doi.org/10.1007/978-3-642-10707-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10706-1

  • Online ISBN: 978-3-642-10707-8

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