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European Conference on Genetic Programming

EuroGP 2013: Genetic Programming pp 49–60Cite as

PhenoGP: Combining Programs to Avoid Code Disruption

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7831))

Abstract

In conventional Genetic Programming (GP), n programs are simultaneously evaluated and only the best programs will survive from one generation to the next. It is a pity as some programs might contain useful code that might be hidden or not evaluated due to the presence of introns. For example in regression, 0× (perfect code) will unfortunately not be assigned a good fitness and this program might be discarded due to the evolutionary process. In this paper, we develop a new form of GP called PhenoGP (PGP). PGP individuals consist of ordered lists of programs to be executed in which the ultimate goal is to find the best order from simple building-blocks programs. If the fitness remains stalled during the run, new building-blocks programs are generated. PGP seems to compare fairly well with canonical GP.

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References

  1. Angeline, P.J., Pollack, J.B.: Coevolving high-level representations. In: Langton, C.G. (ed.) Artificial Life III, Santa Fe, New Mexico, June 15-19. SFI Studies in the Sciences of Complexity, vol. XVII, pp. 55–71. Addison-Wesley (1992, 1994)

    Google Scholar 

  2. Brameier, M., Banzhaf, W.: Linear Genetic Programming. Genetic and Evolutionary Computation. Springer (2007)

    Google Scholar 

  3. Chen, G., Zhang, M.: Evolving While-Loop Structures in Genetic Programming for Factorial and Ant Problems. In: Zhang, S., Jarvis, R.A. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 1079–1085. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Ciesielski, V., Li, X.: Experiments with explicit for-loops in genetic programming. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation, Portland, Oregon, pp. 494–501. IEEE Press (2004)

    Google Scholar 

  5. Downey, C., Zhang, M.: Parallel Linear Genetic Programming. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds.) EuroGP 2011. LNCS, vol. 6621, pp. 178–189. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Kadar, I., Ben-Shahar, O., Sipper, M.: Evolution of a local boundary detector for natural images via genetic programming and texture cues. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, pp. 1887–1888. ACM, New York (2009)

    Google Scholar 

  7. Koza, J.: Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press (1994)

    Google Scholar 

  8. Langdon, W.B., Poli, R.: Why ants are hard. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, Wisconsin, USA, July 22-25, pp. 193–201. Morgan Kaufmann (1998)

    Google Scholar 

  9. Larres, J., Zhang, M., Browne, W.: Using unrestricted loops in genetic programming for image classification. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)

    Google Scholar 

  10. Lichodzijewski, P., Heywood, M.I.: Managing team-based problem solving with symbiotic bid-based genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 363–370. Morgan Kaufmann (2008)

    Google Scholar 

  11. Miller, J.: Cartesian Genetic Programming. Springer (2011)

    Google Scholar 

  12. Spector, L.: Automatic Quantum Computer Programming: A Genetic Programming Approach. Springer (2006)

    Google Scholar 

  13. Spector, L., Clark, D.M., Lindsay, I., Barr, B., Klein, J.: Genetic programming for finite algebras. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 1291–1298. ACM, New York (2008)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Univ Lille Nord de France, ULCO, LISIC, BP 719, F-62228, Calais, France

    Cyril Fonlupt & Denis Robilliard

Authors
  1. Cyril Fonlupt
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  2. Denis Robilliard
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Editor information

Editors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Piotowo 2, 60-965, Poznań, Poland

    Krzysztof Krawiec

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

    Alberto Moraglio

  3. Geisel School of Medicine, Dartmouth College, 03755, Hanover, NH, USA

    Ting Hu

  4. Department of Computer Engineering, Istanbul Technical University, 34469, Masla, Istanbul, Turkey

    A. Şima Etaner-Uyar

  5. Institute of Computer Graphics and Algorithms, Vienna University of Technology, 1040, Vienna, Austria

    Bin Hu

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© 2013 Springer-Verlag Berlin Heidelberg

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Fonlupt, C., Robilliard, D. (2013). PhenoGP: Combining Programs to Avoid Code Disruption. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds) Genetic Programming. EuroGP 2013. Lecture Notes in Computer Science, vol 7831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37207-0_5

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  • DOI: https://doi.org/10.1007/978-3-642-37207-0_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37206-3

  • Online ISBN: 978-3-642-37207-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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