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Multiple Interactive Outputs in a Single Tree: An Empirical Investigation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4445))

Abstract

This paper describes Multiple Interactive Outputs in a Single Tree (MIOST), a new form of Genetic Programming (GP). Our approach is based on two ideas. Firstly, we have taken inspiration from graph-GP representations. With this idea we decided to explore the possibility of representing programs as graphs with oriented links. Secondly, our individuals could have more than one output. This idea was inspired on the divide and conquer principle, a program is decomposed in subprograms, and so, we are expecting to make the original problem easier by breaking down a problem into two or more sub-problems. To verify the effectiveness of our approach, we have used several evolvable hardware problems of different complexity taken from the literature. Our results indicate that our approach has a better overall performance in terms of consistency to reach feasible solutions.

Kerwords: Multiple Interactive Outputs in a Single Tree, Genetic Programming, Graph-GP representations.

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References

  1. Angeline, P.J.: Multiple interacting programs: A representation for evolving complex behaviors. Cybernetics and Systems 29(8), 779–806 (1998)

    Article  MATH  Google Scholar 

  2. Angeline, P.J., Pollack, J.B.: Coevolving high-level representations. July Technical report 92-PA-COEVOLVE, Laboratory for Artificial Intelligence. The Ohio State University (1993)

    Google Scholar 

  3. Angeline, P.J., Pollack, J.B.: Evolutionary module acquisition. In: Fogel, D., Atmar, W. (eds.) Proceedings of the Second Annual Conference on Evolutionary Programming, La Jolla, CA, USA, 25-26 February, pp. 154–163 (1993)

    Google Scholar 

  4. Coello, C., Aguirre, A.: Design of combinational logic circuits through and evolutionary multiobjective optimization approach. Artificial Intelligence for Engineering, Design, Analysis and Manufacture 16, 39–53 (2002)

    Google Scholar 

  5. Galvan-Lopez, E., Poli, R., Coello, C.C.: Reusing code in genetic programming. In: Keijzer, M., O’Reilly, U.-M., Lucas, S.M., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 359–368. Springer, Heidelberg (2004)

    Google Scholar 

  6. Kantschik, W., Banzhaf, W.: Linear-tree GP and its comparison with other GP structures. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 302–312. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  7. Kantschik, W., Banzhaf, W.: Linear-graph GP—A new GP structure. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 83–92. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  8. Kimura, M.: Evolutionary rate at the molecular level. Nature 217, 624–626 (1968)

    Article  Google Scholar 

  9. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  10. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

  11. Luna, E.H., Coello, C.C., Aguirre, A.H.: On the use of a population-based particle swarm optimizer to design combinational logic circuits. In: Zebulum, R.S., et al. (eds.) Proceedings of the 2004 NASA/DoD Conference on Evolvable Hardware, pp. 183–190. IEEE Computer Society, Los Alamitos (June 2004)

    Chapter  Google Scholar 

  12. 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)

    Google Scholar 

  13. Montana, D.J.: Strongly typed genetic programming. BBN Technical Report #7866, Bolt Beranek and Newman, Cambridge, MA 02138, USA (7 May, 1993)

    Google Scholar 

  14. Poli, R.: Parallel distributed genetic programming. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimisation, McGraw-Hill, New York (1999)

    Google Scholar 

  15. Teller, A., Veloso, M.: PADO: Learning tree structured algorithms for orchestration into an object recognition system. Technical Report CMU-CS-95-101, Department of CS, Carnegie Mellon University, Pittsburgh, PA, USA (1995)

    Google Scholar 

  16. Xu, X., Eberhart, R.C., Shi, Y.: Swarm intelligence for permutation optimization: A case study on n-queens problem. In: Proceedings of the IEEE Swarm Intelligence Symposium 2003, Indianapolis, Indiana, USA, pp. 243–246. IEEE Press, Los Alamitos (2003)

    Google Scholar 

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Marc Ebner Michael O’Neill Anikó Ekárt Leonardo Vanneschi Anna Isabel Esparcia-Alcázar

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Galván-López, E., Rodríguez-Vázquez, K. (2007). Multiple Interactive Outputs in a Single Tree: An Empirical Investigation. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_32

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  • DOI: https://doi.org/10.1007/978-3-540-71605-1_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71602-0

  • Online ISBN: 978-3-540-71605-1

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