Skip to main content
SpringerLink
Log in

Evolving Graphs by Graph Programming

  • Conference paper
  • First Online:
Book cover Genetic Programming (EuroGP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10781))

Included in the following conference series:

Abstract

Rule-based graph programming is a deep and rich topic. We present an approach to exploiting the power of graph programming as a representation and as an execution medium in an evolutionary algorithm (EGGP). We demonstrate this power in comparison with Cartesian Genetic Programming (CGP), showing that it is significantly more efficient in terms of fitness evaluations on some classic benchmark problems. We hypothesise that this is due to its ability to exploit the full graph structure, leading to a richer mutation set, and outline future work to test this hypothesis, and to exploit further the power of graph programming within an EA.

T. Atkinson—Supported by a Doctoral Training Grant from the Engineering and Physical Sciences Research Council (EPSRC) in the UK.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Atkinson, T., Plump, D., Stepney, S.: Probabilistic graph programming. In: Proceedings of the International Workshop on Graph Computation Models (GCM 2017) (2017)

    Google Scholar 

  2. Bak, C., Plump, D.: Compiling graph programs to C. In: Echahed, R., Minas, M. (eds.) ICGT 2016. LNCS, vol. 9761, pp. 102–117. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40530-8_7

    Chapter  Google Scholar 

  3. Coecke, B., Duncan, R.: Interacting quantum observables: categorical algebra and diagrammatics. New J. Phys. 13(4) (2011). 86 p

    Google Scholar 

  4. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009)

    MATH  Google Scholar 

  5. Jungnickel, D.: Graphs, Networks and Algorithms, 4th edn. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-32278-5

    Book  MATH  Google Scholar 

  6. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1993)

    MATH  Google Scholar 

  7. Koza, J.R., Bennett, F.H., Stiffelman, O.: Genetic programming as a Darwinian invention machine. In: Poli, R., Nordin, P., Langdon, W.B., Fogarty, T.C. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 93–108. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48885-5_8

    Chapter  Google Scholar 

  8. Galván-López, E., Rodríguez-Vázquez, K.: 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.) EuroGP 2007. LNCS, vol. 4445, pp. 341–350. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71605-1_32

    Chapter  Google Scholar 

  9. Machado, P., Correia, J., Assunção, F.: Graph-based evolutionary art. In: Gandomi, A.H., Alavi, A.H., Ryan, C. (eds.) Handbook of Genetic Programming Applications, pp. 3–36. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20883-1_1

    Chapter  Google Scholar 

  10. Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Statist. 18(1), 50–60 (1947)

    Article  MathSciNet  MATH  Google Scholar 

  11. Mano, M.M.: Digital Design. EBSCO Publishing Inc., Ipswich (2002)

    MATH  Google Scholar 

  12. Miller, J.F.: Cartesian Genetic Programming. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-17310-3

    Book  MATH  Google Scholar 

  13. Miller, J.F., Smith, S.L.: Redundancy and computational efficiency in Cartesian Genetic Programming. IEEE Trans. Evol. Comput. 10(2), 167–174 (2006)

    Article  Google Scholar 

  14. 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). https://doi.org/10.1007/978-3-540-46239-2_9

    Chapter  Google Scholar 

  15. O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Trans. Evol. Comput. 5(4), 349–358 (2001)

    Article  Google Scholar 

  16. Pereira, F.B., Machado, P., Costa, E., Cardoso, A.: Graph based crossover - a case study with the busy beaver problem. In: Proceedings of the Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 1149–1155. Morgan Kaufmann (1999)

    Google Scholar 

  17. Plump, D.: The design of GP 2. In: Proceedings of the Workshop on Reduction Strategies in Rewriting and Programming (WRS 2011), EPTCS, vol. 82, pp. 1–16 (2012). https://doi.org/10.4204/EPTCS.82.1

  18. Plump, D.: From imperative to rule-based graph programs. J. Logical Algebraic Methods Program. 88, 154–173 (2017). https://doi.org/10.1016/j.jlamp.2016.12.001

    Article  MathSciNet  MATH  Google Scholar 

  19. Poli, R.: Evolution of graph-like programs with parallel distributed genetic programming. In: Bäck, T. (ed.) Proceedings of the International Conference on Genetic Algorithms, pp. 346–353. Morgan Kaufmann (1997)

    Google Scholar 

  20. Poli, R.: Parallel distributed genetic programming. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 403–431. McGraw-Hill (1999)

    Google Scholar 

  21. Ryan, C., Collins, J.J., Neill, M.O.: Grammatical evolution: evolving programs for an arbitrary language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83–96. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0055930

    Chapter  Google Scholar 

  22. Skiena, S.S.: The Algorithm Design Manual, 2nd edn. Springer, London (2008). https://doi.org/10.1007/978-1-84800-070-4

    Book  MATH  Google Scholar 

  23. Stanley, K.O., Miikkulainen, R.: Efficient reinforcement learning through evolving neural network topologies. In: Proceedings of the Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 569–577. Morgan Kaufmann Publishers Inc. (2002)

    Google Scholar 

  24. Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)

    Article  Google Scholar 

  25. Syvanen, M., Kado, C.I.: Horizontal Gene Transfer. Academic Press, London (2001)

    Google Scholar 

  26. Turner, A.J., Miller, J.F.: Introducing a cross platform open source Cartesian Genetic Programming library. Genet. Program Evolvable Mach. 16(1), 83–91 (2015)

    Article  Google Scholar 

  27. Vargha, A., Delaney, H.D.: A critique and improvement of the CL common language effect size statistics of McGraw and Wong. J. Educ. Behav. Stat. 25(2), 101–132 (2000)

    Google Scholar 

  28. Walker, J.A., Miller, J.F.: Evolution and acquisition of modules in Cartesian Genetic Programming. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 187–197. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24650-3_17

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of York, York, UK

    Timothy Atkinson, Detlef Plump & Susan Stepney

Authors
  1. Timothy Atkinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Detlef Plump
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Susan Stepney
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Timothy Atkinson .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Lisbon, Portugal

    Mauro Castelli

  2. Brno University of Technology, Brno, Czech Republic

    Lukas Sekanina

  3. Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. University of Parma, Parma, Italy

    Stefano Cagnoni

  5. University of Cádiz, Cádiz, Spain

    Pablo García-Sánchez

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Atkinson, T., Plump, D., Stepney, S. (2018). Evolving Graphs by Graph Programming. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science(), vol 10781. Springer, Cham. https://doi.org/10.1007/978-3-319-77553-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77553-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77552-4

  • Online ISBN: 978-3-319-77553-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation