Skip to main content
SpringerLink
Log in

Context Matters: Adaptive Mutation for Grammars

  • Conference paper
  • First Online:
Genetic Programming (EuroGP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13986))

Included in the following conference series:

Abstract

This work proposes Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation. In SGE, the genotype of individuals contains a list for each non-terminal of the grammar that defines the search space. In our proposed mutation, each individual contains an array with a different, self-adaptive mutation rate for each non-terminal. We also propose Function Grouped Grammars, a grammar design procedure to enhance the benefits of the propose mutation. Experiments were conducted on three symbolic regression benchmarks using Probabilistic Structured Grammatical Evolution (PSGE), a variant of SGE. Results show our approach is similar or better when compared with the standard grammar and mutation.

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 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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. Whigham, P.A., Science, D.O.C.: Grammatically-based genetic programming (1995)

    Google Scholar 

  2. 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 

  3. McKay, R.I., Hoai, N.X., Whigham, P.A., Shan, Y., O’Neill, M.: Grammar-based genetic programming: a survey. Genet. Program. Evolvable Mach. 11(3–4), 365–396 (2010). https://doi.org/10.1007/s10710-010-9109-y

    Article  Google Scholar 

  4. Whigham, P.A., Dick, G., Maclaurin, J., Owen, C.A.: Examining the “best of both worlds” of grammatical evolution. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1111–1118. ACM (2015)

    Google Scholar 

  5. Lourenço, N., Assunção, F., Pereira, F.B., Costa, E., Machado, P.: Structured grammatical evolution: a dynamic approach. In: Ryan, C., O’Neill, M., Collins, J.J. (eds.) Handbook of Grammatical Evolution, pp. 137–161. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78717-6_6

    Chapter  Google Scholar 

  6. Lourenço, N., Ferrer, J., Pereira, F.B., Costa, E.: A comparative study of different grammar-based genetic programming approaches. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 311–325. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55696-3_20

    Chapter  Google Scholar 

  7. Medvet, E.: A comparative analysis of dynamic locality and redundancy in grammatical evolution. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 326–342. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55696-3_21

    Chapter  Google Scholar 

  8. Lourenço, N., Pereira, F.B., Costa, E.: Unveiling the properties of structured grammatical evolution. Genet. Program. Evolvable Mach. 17(3), 251–289 (2016). https://doi.org/10.1007/s10710-015-9262-4

    Article  Google Scholar 

  9. Gerhart, J., Kirschner, M.: The theory of facilitated variation. Proc. Natl. Acad. Sci. 104(1), 8582–8589 (2007). https://doi.org/10.1073/pnas.0701035104

    Article  Google Scholar 

  10. Nicolau, M., Agapitos, A.: Understanding grammatical evolution: grammar design. In: Ryan, C., O’Neill, M., Collins, J.J. (eds.) Handbook of Grammatical Evolution, pp. 23–53. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78717-6_2

    Chapter  Google Scholar 

  11. Nicolau, M.: Automatic grammar complexity reduction in grammatical evolution. In: The 3rd Grammatical Evolution Workshop: A Workshop of the 2004 Genetic and Evolutionary Computation Conference (GECCO 2004), Seattle, Washington, USA, 26–30 June 2004. Seattle, Washington, USA (2004)

    Google Scholar 

  12. Dick, G., Whigham, P.A.: Initialisation and grammar design in grammar-guided evolutionary computation. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2022), pp. 534–537. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3520304.3529051

  13. Hemberg, E.: Pre-, in-and postfix grammars for symbolic regression in grammatical evolution (2008)

    Google Scholar 

  14. Ryan, C., O’Neill, M., Collins, J.J. (eds.): Handbook of Grammatical Evolution. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78717-6

    Book  MATH  Google Scholar 

  15. Megane, J., Lourenco, N., Machado, P.: Probabilistic structured grammatical evolution. In: 2022 IEEE Congress on Evolutionary Computation (CEC), pp. 991–999. IEEE (2022). https://doi.org/10.1109/cec55065.2022.9870397

  16. Thorhauer, A.: On the non-uniform redundancy in grammatical evolution. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 292–302. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45823-6_27

    Chapter  Google Scholar 

  17. Thorhauer, A., Rothlauf, F.: On the locality of standard search operators in grammatical evolution. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 465–475. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10762-2_46

    Chapter  Google Scholar 

  18. Rothlauf, F., Oetzel, M.: On the locality of grammatical evolution. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 320–330. Springer, Heidelberg (2006). https://doi.org/10.1007/11729976_29

    Chapter  Google Scholar 

  19. Nicolau, M.: Understanding grammatical evolution: initialisation. Genet. Program. Evolvable Mach. 18(4), 467–507 (2017). https://doi.org/10.1007/s10710-017-9309-9

    Article  Google Scholar 

  20. O’Neill, M., Brabazon, A., Nicolau, M., Garraghy, S.M., Keenan, P.: \(\pi \)grammatical evolution. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3103, pp. 617–629. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24855-2_70

    Chapter  Google Scholar 

  21. Fagan, D., O’Neill, M., Galván-López, E., Brabazon, A., McGarraghy, S.: An analysis of genotype-phenotype maps in grammatical evolution. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 62–73. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12148-7_6

    Chapter  Google Scholar 

  22. Mégane, J., Lourenço, N., Machado, P.: Probabilistic grammatical evolution. In: Hu, T., Lourenço, N., Medvet, E. (eds.) EuroGP 2021. LNCS, vol. 12691, pp. 198–213. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72812-0_13

    Chapter  Google Scholar 

  23. Mégane, J., Lourenço, N., Machado, P.: Co-evolutionary probabilistic structured grammatical evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 991–999. ACM (2022). https://doi.org/10.1145/3512290.3528833

  24. Beyer, H., Schwefel, H.: Evolution strategies - a comprehensive introduction. Nat. Comput. 1, 3–52 (2004). https://doi.org/10.1023/A:1015059928466

    Article  MathSciNet  MATH  Google Scholar 

  25. Hinterding, R.: Gaussian mutation and self-adaption for numeric genetic algorithms. In: Proceedings of 1995 IEEE International Conference on Evolutionary Computation, vol. 1, p. 384. IEEE (1995)

    Google Scholar 

  26. Teo, J.: Self-adaptive mutation for enhancing evolutionary search in real-coded genetic algorithms. In: 2006 International Conference on Computing & Informatics, pp. 1–6. IEEE (2006)

    Google Scholar 

  27. Libelli, S.M., Alba, P.: Adaptive mutation in genetic algorithms. Soft Comput. 4(2), 76–80 (2000). https://doi.org/10.1007/s005000000042

    Article  Google Scholar 

  28. Lis, J.: Genetic algorithm with the dynamic probability of mutation in the classification problem. Pattern Recogn. Lett. 16(12), 1311–1320 (1995)

    Article  Google Scholar 

  29. Stark, N., Minetti, G.F., Salto, C.: A new strategy for adapting the mutation probability in genetic algorithms (2012)

    Google Scholar 

  30. Cruz-Salinas, A.F., Perdomo, J.G.: Self-adaptation of genetic operators through genetic programming techniques. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 913–920. ACM (2017)

    Google Scholar 

  31. Gomez, J.: Self adaptation of operator rates in evolutionary algorithms. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3102, pp. 1162–1173. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24854-5_113

    Chapter  Google Scholar 

  32. Gómez, J., León, E.: On the class of hybrid adaptive evolutionary algorithms (chavela). Nat. Comput. 20(3), 377–394 (2021). https://doi.org/10.1007/s11047-021-09843-5

    Article  MathSciNet  Google Scholar 

  33. Montero, E., Riff, M.C.: Calibrating strategies for evolutionary algorithms. In: 2007 IEEE Congress on Evolutionary Computation. IEEE (2007)

    Google Scholar 

  34. Montero, E., Riff, M.-C.: Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems. In: An, A., Matwin, S., Raś, Z.W., Ślȩzak, D. (eds.) ISMIS 2008. LNCS (LNAI), vol. 4994, pp. 262–267. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68123-6_29

  35. Coelho, V.N., et al.: Hybrid self-adaptive evolution strategies guided by neighborhood structures for combinatorial optimization problems. Evol. Comput. 24(4), 637–666 (2016)

    Article  Google Scholar 

  36. Fagan, D., Hemberg, E., Nicolau, M., O’Neill, M., McGarraghy, S.: Towards adaptive mutation in grammatical evolution. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion (GECCO Companion 2012). ACM Press (2012)

    Google Scholar 

  37. Manzoni, L., Bartoli, A., Castelli, M., Goncalves, I., Medvet, E.: Specializing context-free grammars with a (1 + 1)-EA. IEEE Trans. Evol. Comput. 24(5), 960–973 (2020)

    Article  Google Scholar 

  38. Tiso, S., Carvalho, P., Lourenço, N., Machado, P.: Structured mutation inspired by evolutionary theory enriches population performance and diversity. arXiv preprint arXiv:2302.00559 (2023)

  39. White, D.R., et al.: Better GP benchmarks: community survey results and proposals. Genet. Program. Evolvable Mach. 14(1), 3–29 (2012). https://doi.org/10.1007/s10710-012-9177-2

    Article  MathSciNet  Google Scholar 

  40. McDermott, J., et al.: Genetic programming needs better benchmarks. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference (GECCO 2012), , pp. 791–798. ACM Press (2012)

    Google Scholar 

  41. Harrison, D., Jr., Rubinfeld, D.L.: Hedonic housing prices and the demand for clean air. J. Environ. Econ. Manag. 5(1), 81–102 (1978)

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work was funded by FEDER funds through the Operational Programme Competitiveness Factors - COMPETE and national funds by FCT - Foundation for Science and Technology (POCI-01-0145-FEDER-029297, CISUC - UID/CEC/00326/2020) and within the scope of the project A4A: Audiology for All (CENTRO-01-0247-FEDER-047083) financed by the Operational Program for Competitiveness and Internationalisation of PORTUGAL 2020 through the European Regional Development Fund.

The first author is funded by FCT, Portugal, under the grant UI/BD/151053/2021 and the second under the grant 2022.10174.BD.

Author information

Authors and Affiliations

  1. Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Pedro Carvalho, Jessica Mégane, Nuno Lourenço & Penousal Machado

Authors
  1. Pedro Carvalho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jessica Mégane
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nuno Lourenço
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Penousal Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedro Carvalho .

Editor information

Editors and Affiliations

  1. Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Gisele Pappa

  2. Università degli studi di Torino, Turin, Italy

    Mario Giacobini

  3. Brno University of Technology, Brno, Czech Republic

    Zdenek Vasicek

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Carvalho, P., Mégane, J., Lourenço, N., Machado, P. (2023). Context Matters: Adaptive Mutation for Grammars. In: Pappa, G., Giacobini, M., Vasicek, Z. (eds) Genetic Programming. EuroGP 2023. Lecture Notes in Computer Science, vol 13986. Springer, Cham. https://doi.org/10.1007/978-3-031-29573-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-29573-7_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29572-0

  • Online ISBN: 978-3-031-29573-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation