Skip to main content
SpringerLink
Log in

Automatic Derivation of Search Objectives for Test-Based Genetic Programming

  • Conference paper
  • First Online:
Genetic Programming (EuroGP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9025))

Included in the following conference series:

Abstract

In genetic programming (GP), programs are usually evaluated by applying them to tests, and fitness function indicates only how many of them have been passed. We posit that scrutinizing the outcomes of programs’ interactions with individual tests may help making program synthesis more effective. To this aim, we propose DOC, a method that autonomously derives new search objectives by clustering the outcomes of interactions between programs in the population and the tests. The derived objectives are subsequently used to drive the selection process in a single- or multiobjective fashion. An extensive experimental assessment on \(15\) discrete program synthesis tasks representing two domains shows that DOC significantly outperforms conventional GP and implicit fitness sharing.

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. Bucci, A., Pollack, J.B., de Jong, E.: Automated extraction of problem structure. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 501–512. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  2. de Jong, E.D., Bucci, A.: DECA: dimension extracting coevolutionary algorithm. In: Cattolico, M., et al., (eds.) GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 313–320. ACM Press, Seattle, Washington, USA (2006)

    Google Scholar 

  3. de Jong, E.D., Pollack, J.B.: Ideal evaluation from coevolution. Evol. Comput. 12(2), 159–192 (2004)

    Article  Google Scholar 

  4. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  5. Ficici, S.G., Pollack, J.B.: Challenges in coevolutionary learning: arms-race dynamics, open-endedness, and mediocre stable states. In: Proceedings of the Sixth International Conference on Artificial Life, pp. 238–247. MIT Press (1998)

    Google Scholar 

  6. Ficici, S.G., Pollack, J.B.: Pareto optimality in coevolutionary learning. In: Kelemen, J., Sosík, P. (eds.) ECAL 2001. LNCS (LNAI), vol. 2159, p. 316. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  7. Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods, vol. 751. John Wiley & Sons, Weinheim (2013)

    Google Scholar 

  8. Jaśkowski, W., Krawiec, K.: Formal analysis, hardness and algorithms for extracting internal structure of test-based problems. Evol. Comput. 19(4), 639–671 (2011)

    Article  Google Scholar 

  9. Kanji, G.K.: 100 Statistical Tests. Sage, London (2006)

    Google Scholar 

  10. Krawiec, K., Lichocki, P.: Using co-solvability to model and exploit synergetic effects in evolution. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 492–501. Springer, Heidelberg (2010)

    Google Scholar 

  11. Krawiec, K., O’Reilly, U.M.: Behavioral programming: a broader and more detailed take on semantic GP. In: Igel, C. (ed.) GECCO 2014: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, pp. 935–942. ACM, Vancouver, BC, Canada, 12–16 July 2014

    Google Scholar 

  12. Krawiec, K., O’Reilly, U.-M.: Behavioral search drivers for genetic programing. In: Nicolau, M., Krawiec, K., Heywood, M.I., Castelli, M., García-Sánchez, P., Merelo, J.J., Rivas Santos, V.M., Sim, K. (eds.) EuroGP 2014. LNCS, vol. 8599, pp. 210–221. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  13. Krawiec, K., Swan, J.: Pattern-guided genetic programming. In: Blum, C. (ed.) GECCO 2013: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, pp. 949–956. ACM, Amsterdam, The Netherlands, 6–10 July 2013

    Google Scholar 

  14. Lasarczyk, C.W.G., Dittrich, P., Banzhaf, W.: Dynamic subset selection based on a fitness case topology. Evol. Comput. 12(2), 223–242 (2004)

    Article  Google Scholar 

  15. Liskowski, P., Krawiec, K.: Discovery of implicit objectives by compression of interaction matrix in test-based problems. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 611–620. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  16. McKay, R.I.B.: Committee learning of partial functions in fitness-shared genetic programming. In: Industrial Electronics Society, 2000. IECON 2000. 26th Annual Conference of the IEEE Third Asia-Pacific Conference on Simulated Evolution and Learning 2000. vol. 4, pp. 2861–2866. IEEE Press, Nagoya, Japan, 22–28 October 2000

    Google Scholar 

  17. McKay, R.I.B.: Fitness sharing in genetic programming. In: Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.G. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pp. 435–442. Morgan Kaufmann, Las Vegas, Nevada, USA, 10–12 July 2000

    Google Scholar 

  18. Noble, J., Watson, R.A.: Pareto coevolution: using performance against coevolved opponents in a game as dimensions for pareto selection. In: Spector, L., et al., (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 493–500. Morgan Kaufmann, San Francisco, California, USA, 7–11 July 2001

    Google Scholar 

  19. Pelleg, D., Moore, A.W., et al.: X-means: extending k-means with efficient estimation of the number of clusters. In: ICML, pp. 727–734 (2000)

    Google Scholar 

  20. Smith, R.E., Forrest, S., Perelson, A.S.: Searching for diverse, cooperative populations with genetic algorithms. Evol. Comput. 1(2), 127–149 (1993)

    Article  Google Scholar 

  21. Spector, L., Clark, D.M., Lindsay, I., Barr, B., Klein, J.: Genetic programming for finite algebras. In: Keijzer, M. (ed.) GECCO 2008: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1291–1298. ACM, Atlanta, GA, USA, 12–16 July 2008

    Google Scholar 

  22. Tomassini, M., Vanneschi, L., Collard, P., Clergue, M.: A study of fitness distance correlation as a difficulty measure in genetic programming. Evol. Comput. 13(2), 213–239 (2005)

    Article  Google Scholar 

  23. Watson, R.A., Pollack, J.B.: Coevolutionary dynamics in a minimal substrate. In: Spector, L., et al., (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 702–709. Morgan Kaufmann, San Francisco, California, USA, 7–11 July 2001

    Google Scholar 

Download references

Acknowledgments

P. Liskowski acknowledges support from grant no. 09/91/DSPB/0572.

Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, Poznań, Poland

    Krzysztof Krawiec & Paweł Liskowski

Authors
  1. Krzysztof Krawiec
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paweł Liskowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krzysztof Krawiec .

Editor information

Editors and Affiliations

  1. Universidade de Coimbra, Coimbra, Portugal

    Penousal Machado

  2. Dalhousie University, Halifax, Nova Scotia, Canada

    Malcolm I. Heywood

  3. University College Dublin, Dublin, Ireland

    James McDermott

  4. Universidade Nova de Lisboa, Lisboa, Portugal

    Mauro Castelli

  5. Universidad de Granada, Granada, Spain

    Pablo García-Sánchez

  6. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

  7. IT University of Copenhagen, Copenhagen, Denmark

    Sebastian Risi

  8. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Krawiec, K., Liskowski, P. (2015). Automatic Derivation of Search Objectives for Test-Based Genetic Programming. In: Machado, P., et al. Genetic Programming. EuroGP 2015. Lecture Notes in Computer Science(), vol 9025. Springer, Cham. https://doi.org/10.1007/978-3-319-16501-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16501-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16500-4

  • Online ISBN: 978-3-319-16501-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation