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Dual network representation applied to the evolution of neural controllers

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Evolutionary Programming VII (EP 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1447))

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Abstract

This paper presents a new approach to the evolution of neural networks. A linear chromosome combined with a grid-based representation of the network and a new crossover operator allow the evolution of the architecture and the weights simultaneously. There is no need for a separate weight optimization procedure and networks with more than one type of activation function can be evolved. This paper describes the representation, the crossover operator, and reports on results of the application of the method to evolve a neural controller for the pole-balancing problem.

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References

  1. D. Fogel. Evolutionary computation: toward a new philosophy of machine Intelligence. IEEE Press, Piscataway, NJ, USA, 1995.

    Google Scholar 

  2. D. Goldberg. Genetic algorithm in search, optimization and machine learning. Addison-Wesley, Reading, Massachusets, 1989.

    Google Scholar 

  3. P. J. Angeline, G. M. Saunders, and J. B. Pollack. An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 5(1), 1994.

    Google Scholar 

  4. D. Fogel. Using evolutionary programming to create neural networks that are capable of playing Tic Tac Toe. In IEEE International Conference on Neural Networks (ICNN), pages. 875–880. IEEE Press, 1993.

    Google Scholar 

  5. J. McDonnell and D. Waagen. Neural network structure design by evolutionary programming. In D. Fogel and W. Atmar, editors, Proceedings of the Sec. Annual Conference on Evolutionary Programming, pages 79–89, La Jolla, CA, USA, Feb. 1993. Evolutionary Programming Society.

    Google Scholar 

  6. V. Maniezzo. Genetic evolution of the topology and weight distribution of neural networks. IEEE Transactions on Neural Networks, 5(1):39–53, 1994.

    Google Scholar 

  7. D. Whitley, S. Dominic, R. Das, and C. Anderson. Genetic reinforcement learning for neurocontrol problems. Machine Learning, 13:259–284, 1993.

    Google Scholar 

  8. J. R. Koza. Genetic Programming, on the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge, Massachusets, 1992.

    Google Scholar 

  9. B. Zhang and H. Muehlenbein. Genetic programming of minimal neural nets using Occam's razor. In S. Forrest, editor, Proceedings of the 5th international conference on genetic algorithms (ICGA'93), pages 342–349. Morgan Kaufmann, 1993.

    Google Scholar 

  10. F. Gruau. Neural network synthesis using cellular encoding and the genetic algorithm. PhD thesis, Laboratoire de L'informatique du Parallélisme, Ecole Normale Supériere de Lyon, Lyon, France, 1994.

    Google Scholar 

  11. R. Poli. Some steps towards a form of parallel distributed genetic programming. In Proceedings of the First On-line Workshop on Soft Computing, pages 290–295, Aug. 1996.

    Google Scholar 

  12. R. Poli. Discovery of symbolic, neuron-symbolic and neural networks with parallel distributed genetic programming. In 3rd International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA), 1997.

    Google Scholar 

  13. J. C. F. Pujol and R. Poli. Evolution of the topology and the weights of neural networks using genetic programming with a dual representation. Technical report CSRP-97-07, The University of Birmingham, School of Computer Science, 1997.

    Google Scholar 

  14. D. Whitley, F. Gruau, and L. Pyeatt. Cellular encoding applied to neurocontrol. In Proceedings of 6th International Conference on Genetic Algorithms, pages 460–467. Morgankaufmann, 1995.

    Google Scholar 

  15. F. Gruau, D. Whitley, and L. Pyeatt. A comparison between cellular encoding and direct encoding for genetic neural networks. In J. Koza, D. Goldberg, D. Fogel, and R. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 81–89, Stanford University, CA, USA, Jul. 1996. MIT Press.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, The University of Birmingham, B15 2TT, Birmingham, UK

    João Carlos Figueira Pujol & Riccardo Poli

Authors
  1. João Carlos Figueira Pujol
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  2. Riccardo Poli
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Editor information

V. W. Porto N. Saravanan D. Waagen A. E. Eiben

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© 1998 Springer-Verlag Berlin Heidelberg

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Pujol, J.C.F., Poli, R. (1998). Dual network representation applied to the evolution of neural controllers. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040815

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  • DOI: https://doi.org/10.1007/BFb0040815

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64891-8

  • Online ISBN: 978-3-540-68515-9

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