Skip to main content

Advertisement

SpringerLink
Log in

Grammar-based immune programming

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

This paper describes Grammar-based Immune Programming (GIP) for evolving programs in an arbitrary language by immunological inspiration. GIP is based on Grammatical Evolution (GE) in which a grammar is used to define a language and decode candidate solutions to a valid representation (program). However, by default, GE uses a Genetic Algorithm in the search process while GIP uses an artificial immune system. Some modifications are needed of an immune algorithm to use a grammar in order to efficiently decode antibodies into programs. Experiments are performed to analyze algorithm behavior over different aspects and compare it with GEVA, a well known GE implementation. The methods are applied to identify a causal model (an ordinary differential equation) from an observed data set, to symbolically regress an iterated function f(f(x)) = g(x), and to find a symbolic representation of a discontinuous function.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Notes

  1. http://www.ncra.ucd.ie/Site/GEVA.html.

  2. Kleene star is a unary operation widely used for regular expressions in which if N is a set of symbols or characters, then N * is the set of all strings composed by symbols in N, including the empty string.

  3. http://www.jscheme.sourceforge.net/jscheme/main.html.

  4. http://www.r-project.org/.

References

  • Amarteifio S, O’Neill M (2005) Coevolving antibodies with a rich representation of grammatical evolution. In: Corne D, Michalewicz Z, Dorigo M, Eiben G, Fogel D, Fonseca C, Greenwood G, Chen TK, Raidl G, Zalzala A, Lucas S, Paechter B, Willies J, Guervos JJM, Eberbach E, McKay B, Channon A, Tiwari A, Volkert LG, Ashlock D, Schoenauer M (eds) Proceedings of the 2005 IEEE Congress on Evolutionary Computation, IEEE Press, Edinburgh, UK, vol 1, pp 904–911

  • Bernardino HS, Barbosa HJ (2009a) Grammar-based immune programming for symbolic regression. In: Proceedings of the 2009 International Conference on Artificial Immune Systems, LNCS 5666, Springer, Berlin Heidelberg, pp 274–287

  • Bernardino HS, Barbosa HJ (2009b) Nature-inspired algorithms for optimisation. Springer, Berlin Heidelberg, chap Artificial Immune Systems for Optimization, pp 389–411

  • Bielecki A, Strug B (2005) An evolutionary algorithm for solving the inverse problem for iterated function systems for a two dimensional image. In: Proceedings of the 4th International Conference on Computer Recognition Systems CORES 05, Springer, Berlin Heidelberg, pp 347–354

  • Bielecki A, Strug B (2007) Finding an iterated function systems based representation for complex visual structures using an evolutionary algorihm. MG&V 16:171–189, http://www.portal.acm.org/citation.cfm?id=1375845.1375856

    Google Scholar 

  • Brown R (1999) On solving nonlinear functional, finite difference, composition, and iterated equations. Fractals 7:277–282

    Article  MathSciNet  MATH  Google Scholar 

  • Burnet FM (1959) The clonal selection theory of acquired immunity. Cambridge University Press, Cambridge

    Google Scholar 

  • de Castro LN, Timmis J (2002) An artificial immune network for multimodal function optimization. In: Proc. of the 2002 IEEE World Congress on Computational Intelligence, Honolulu, Hawaii, USA, vol I, pp 669–674

  • de Castro LN, von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evo Comp 6(3):239–251

    Article  Google Scholar 

  • Chomsky N (2002) Syntactic structures. Mouton de Gruyter, Berlin, New York

    Book  Google Scholar 

  • Ciccazzo A, Conca P, Nicosia G, Stracquadanio G (2008) An advanced clonal selection algorithm with ad-hoc network-based hypermutation operators for synthesis of topology and sizing of analog electrical circuits. In: Bentley PJ, Lee D, Jung S (eds) Proceedings of the international conference on artificial immune systems—ICARIS 2008, Springer, LNCS, vol 5132, pp 60–70

  • Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. In: Proceedings of the 1st international conference on genetic algorithms, L. Erlbaum Associates Inc., Hillsdale, NJ, USA, pp 183–187

  • Cutello V, Narzisi G, Nicosia G, Pavone M (2005) Clonal selection algorithms: a comparative case study using effective mutation potentials. In: Proceedings of the international conference on artificial immune systems—ICARIS 2005, Springer, LNCS, vol 3627, pp 13–28

  • Dasgupta D, Nino F (2008) Immunological computation: theory and applications. Auerbach Publications, Boston, MA, USA

    Book  Google Scholar 

  • Elseth GD, Baumgardner KD (1995) Principles of modern genetics. Brooks Cole, Pacific Grove, CA

    Google Scholar 

  • Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. ArXiv Computer Science e-prints arXiv:cs/0102027

  • Gan Z, Chow TW, Chau W (2009a) Clone selection programming and its application to symbolic regression. Expert Syst Appl 36(2):3996–4005

    Article  Google Scholar 

  • Gan Z, Zhao MB, Chow TW (2009b) Induction machine fault detection using clone selection programming. Exp Syst Appl 36(4):8000–8012

    Article  Google Scholar 

  • Johnson CG (2003) Artificial immune system programming for symbolic regression. In: Proceedings of the 6th European conference on genetical programming—EuroGP 2003, pp 345–353

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection (complex adaptive systems). The MIT Press, Cambridge, MA

    Google Scholar 

  • Koza JR, Bennett III FH, Andre D, Keane MA (2000) Synthesis of topology and sizing of analog electrical circuits by means of genetic programming. Comput Methods Appl Mech Eng 186(2–4):459–482

    Article  MATH  Google Scholar 

  • Lau A, Musilek P (2009) Immune programming models of cryptosporidium parvum inactivation by ozone and chlorine dioxide. Inf Sci 179(10):1469–1482

    Article  Google Scholar 

  • McKinney B, Tian D (2008) Grammatical immune system evolution for reverse engineering nonlinear dynamic bayesian models. Cancer Inform 6:433–447

    Google Scholar 

  • Musilek P, Lau A, Reformat M, Wyard-Scott L (2006) Immune programming. Inf Sci 176(8):972–1002

    Article  MathSciNet  MATH  Google Scholar 

  • O’Neill M, Brabazon A (2006) Grammatical differential evolution. In: Proceedings of the 2006 international conference on artificial intelligence—ICAI 2006, CSREA Press, Las Vegas, Nevada, USA, pp 231–236

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

    Article  Google Scholar 

  • O’Neill M, Ryan C (2003) Grammatical evolution: evolutionary automatic programming in an arbitrary language. Kluwer Academic Publishers, Boston, MA

    MATH  Google Scholar 

  • O’Neill M, Brabazon A, Adley C (2004) The automatic generation of programs for classification problems with grammatical swarm. In: Congress on evolutionary computation—CEC 2004, vol 1, pp 104–110

  • O’Neill M, Hemberg E, Gilligan C, Bartley E, McDermott J, Brabazon A (2008) Geva: grammatical evolution in java. SIGEVOlution 3:17–22 doi:10.1145/1527063.1527066

    Google Scholar 

  • Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. http://www.gp-field-guide.org.uk

  • Ryan C, Collins J, Neill MO (1998) Grammatical evolution: evolving programs for an arbitrary language. In: LNCS 1391, Proceedings of the first European workshop on genetic programming, Springer-Verlag, pp 83–95

  • Schmidt MD, Lipson H (2008) Data-mining dynamical systems: automated symbolic system identification for exploratory analysis. In: Proceedings of the Biennial ASME conference on engineering systems design and analysis—ESDA08, Haifa, Israel

  • Schmidt MD, Lipson H (2009) Solving iterated functions using genetic programming. In: Proceedings of the 11th annual conference companion on genetic and evolutionary computation conference: late breaking papers, ACM, New York, NY, USA, GECCO ’09, pp 2149–2154, doi:10.1145/1570256.1570292

  • Smith SF (1980) A learning system based on genetic adaptive algorithms. PhD thesis, University of Pittsburgh, Pittsburgh, PA, USA

  • Smith SF (1983) Flexible learning of problem solving heuristics through adaptive search. In: IJCAI’83: Proceedings of the eighth international joint conference on artificial intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 422–425

Download references

Acknowledgements

The authors thank the support from CNPq (grants 140551/2008-5 and 311651/2006-2) and FAPERJ (grant E-26/100.101/2008).

Author information

Authors and Affiliations

  1. Laboratório Nacional de Computação Científica, Av. Getulio Vargas, 333, 25.651-075, Petrópolis, RJ, Brazil

    Heder S. Bernardino & Helio J. C. Barbosa

Authors
  1. Heder S. Bernardino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Helio J. C. Barbosa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heder S. Bernardino.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bernardino, H.S., Barbosa, H.J.C. Grammar-based immune programming. Nat Comput 10, 209–241 (2011). https://doi.org/10.1007/s11047-010-9217-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-010-9217-x

keywords

Search

Navigation