Skip to main content
SpringerLink
Log in
Book cover

Engineering Evolutionary Intelligent Systems pp 325–335Cite as

A Grammatical Genetic Programming Representation for Radial Basis Function Networks

  • Chapter

  • 469 Accesses

Part of the book series: Studies in Computational Intelligence ((SCI,volume 82))

Summary

We present a hybrid algorithm where evolutionary computation, in the form of grammatical genetic programming, is used to generate Radial Basis Function Networks. An introduction to the underlying algorithms of the hybrid approach is outlined, followed by a description of a grammatical representation for Radial Basis Function networks. The hybrid algorithm is tested on five benchmark classification problem instances, and its performance is found to be encouraging.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Klir G (1988) Fuzzy sets, uncertainty and information. Prentice-Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  2. Bandemer H (1992) Fuzzy data analysis. Kluwer Academic, Dordrecht

    MATH  Google Scholar 

  3. Michalewicz Z (1994) Genetic algorithms + data structures = evolution programs. AI Series. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  4. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  5. Brunak S, Baldi P (2001) Bioinformatics: the machine learning approach. MIT, Cambridge, MA

    MATH  Google Scholar 

  6. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kauffmann, Los Altos, CA

    MATH  Google Scholar 

  7. Beerenwinkel N (2003) Computational analysis of HIV drug resistant data. PhD Thesis, MPS-MPI for Informatics, University of Saarland, Saarbruecken, Germany

    Google Scholar 

  8. Sanchez E (1977) Solutions in composite fuzzy relation equations. In: Gupta, Saridis, Gaines (eds) Fuzzy automata and decision processes. North-Holland, New York, pp 221–234

    Google Scholar 

  9. Sanchez E (1979) Medical diagnosis and composite fuzzy relations. In: Gupta, Ragade, Yager (eds) Advances in fuzzy set theory and applications. North-Holland, New York, pp 437–444

    Google Scholar 

  10. Adlassnig K, Kolarz G (1982) CADIAG-2: Computer-assisted medical diagnosis using fuzzy subsets. In: Gupta, Sanchez (eds) Approximate reasoning in decision analysis. North-Holland, New York, pp 203–217, 219–247

    Google Scholar 

  11. Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  MathSciNet  Google Scholar 

  12. Sanchez E (1984) Solution of fuzzy equations with extended operations. Fuzzy Sets Syst 12:237–248

    Article  MATH  Google Scholar 

  13. Mizumoto M (1989) Pictorial representations of fuzzy connectives, part II: cases of compensatory operators and self-dual operators. Fuzzy Sets Syst 32:45–79

    Article  MATH  MathSciNet  Google Scholar 

  14. Pedrycz W (1993) Fuzzy relational equations. Fuzzy Sets Syst 59:189–195

    Article  MathSciNet  Google Scholar 

  15. Sanchez E (1993) Fuzzy genetic algorithms in soft computing enviroment. Invited Plenary Lecture in Fifth IFSA World Congress, Seoul

    Google Scholar 

  16. Voigt HM (1995) Soft genetic operators in evolutionary algorithms. In: Banzhaf W, Eeckman FH (eds). Evolution and biocomputation. Lecture notes in computer science, vol 899. Springer, Berlin Heidelberg New York, pp 123–121

    Google Scholar 

  17. Herrera F (1996) Dynamic and heuristic fuzzy connective based crossover operators for controlling the diversity and the convergence of real coded algorithms. Int J Intell Syst 11:1013–1041

    Article  MATH  Google Scholar 

  18. Lathrop R, Pazzani MJ (1999) Combinatorial optimization in rapidly mutating drug-resistant viruses. J Combinatorial Optimiz 3:301–320

    Article  MATH  Google Scholar 

  19. Perelson AS, Nelson PW (1999) Mathematical analysis of HIV-1 dynamics in vivo. SIAM Rev 41:3–44

    Article  MATH  MathSciNet  Google Scholar 

  20. Beerenwinkel N, Schmidt B, Walter H, Kaiser R, Lengauer T, Hoffmann D, Korn K, Selbig J (2001) Geno2pheno: interpreting genotypic HIV drug resistance tests. IEEE Intell Syst Biol 16(6):35–41

    Article  Google Scholar 

  21. Beerenwinkel N, Kaiser R, Schmidt B, Walter H, Korn K, Hoffmann D, Lengauer T, Selbig J (2001) Clustering resistance factors: identification of complex categories of drug resistance. Antiviral Ther 6(1):105

    Google Scholar 

  22. Beerenwinkel N, Schmidt B, Walter H, Kaiser R, Lengauer T, Hoffmann D, Korn K, Selbig J (2001) Identifying drug resistance-associated patterns in HIV genotypes. Proceedings of the German conference on bioinformatics, October 7–10, 2001, pp 126–130

    Google Scholar 

  23. Beerenwinkel N, Sing T, Daumer M, Kaiser R, Lengauer T (2004) Computing the genetic barrier. Antiviral Ther 9:S125

    Google Scholar 

  24. Beerenwinkel N, Daumer M, Sierra S, Schmidt B, Walter H, Korn K, Oette M et al. (2002) Geno2pheno is predictive of short-term virological response. Antiviral Ther 7:S74

    Google Scholar 

  25. Beerenwinkel N, Schmidt B, Walter H, Kaiser R, Lengauer T, Hoffmann D et al. (2002) Diversity and complexity of HIV-1 rrug resistance: a bioinformatics approach to predict phenotype from genotype. Proc Natl Acad Sci USA 99(12):8271–8276

    Article  Google Scholar 

  26. Beerenwinkel N, Daumer M et al. (2003) Geno2pheno: estimating phenotypic drug resistance from HIV-1 genotypes. Nucleic Acids Res 31(13):3850–3855

    Article  Google Scholar 

  27. Beerenwinkel N, Lengauer T, Daumer M, Kaiser R, Walter H, Korn K, Hoffmann D, Selbig J (2003) Methods for optimizing antiviral combination therapies. Bioinf 19(1)(ISMB ’03):i16–i25

    Google Scholar 

  28. Beerenwinkel N, Kaiser R, Rahnenfuhrer J, Daumer M, Hoffmann D, Selbig J, Lengauer T (2003) Tree models for the evolution of drug resistance. Antiviral Ther 8:S107

    Google Scholar 

  29. De Luca A, Vendittelli M, Baldini F, Di Giambenedetto S, Trotta MP, Cingolani A, Bacarelli A, Gori C, Perno C F, Antinori A, Ulivi G (2004) Construction, training and clinical validation of an interpretation system for genotypic HIV-1 drug resistance based on fuzzy rules revised by virological outcomes. Antiviral Ther 9(4)

    Google Scholar 

  30. Larder BA, Revell A, Wang D, Harrigan R, Montaner J, Wegner S, Lane C (2005) Neural networks are more accurate predictors of virological response to HAART than rules-based genotype interpretation systems. Poster presentation at 10th european AIDS conference/EACS, Dublin Ireland, 17–20 November

    Google Scholar 

  31. Wang D, Larder BA, Revell A, Harrigan R, Montaner J, Wegner S, Lane C (2005) Treatment history improves the accuracy of neural networks predicting virologic response to HIV therapy. Abstract & Poster presentation at BioSapiens – viRgil workshop on bioinformatics for infectious diseases, Caesar Bonn, Germany, September 21–23

    Google Scholar 

  32. Revell A, Larder BA, Wang D, Wegner S, Harrigan R, Montaner J, Lane C (2005) Global neural network models are superior to single clinic models as general quantitative predictors of virologic treatment response. Poster presentation at third IAS conference on HIV pathogenesis and treatment 24–27 July, Rio de Janeiro, Brazil

    Google Scholar 

  33. Prosperi M, Zazzi M, De Luca A, Di Giambenedetto S, Ulivi G et al. (2005) Common law applied to treatment decisions for drug resistant HIV – antiviral therapy 10:S62 (abstract & poster at XIV International HIV Drug Resistance Workshop, Quebec City)

    Google Scholar 

  34. Prosperi M, Zazzi M, Gonnelli A, Trezzi M, Corsi P, Morfini M, Nerli A, De Gennaro M, Giacometti A, Ulivi G, Di Giambenedetto S, De Luca A (2005) Modelling in vivo HIV evolutionary mutational pathways under AZT-3TC regimen through Markov chains – abstract, poster & selected lecture at BioSapiens-viRgil Workshop on bioinformatics for infectious diseases, Caesar Bonn, Germany, September 21–23

    Google Scholar 

  35. Savenkov I, Beerenwinkel N, Sing T, Daumer M, Rhee SY, Horberg M, Scarsella A, Zolopa A, Lee S Y, Hurley L, Fessel WJ, Shafer RW, Kaiser R, Lengauer T (2005) Probabilistic modelling of genetic barriers enables reliable prediction of HAART outcome at below 15% error rate. Abstract at BioSapiens-viRgil Workshop on bioinformatics for infectious diseases, Caesar Bonn, Germany, September 21–23

    Google Scholar 

  36. IAS-USA. http://iasusa.org

  37. Stanford HIV data base. http://hivdb.stanford.edu

  38. VIRCO Labs. http://vircolab.com

  39. ARCA consortium. http://www.hivarca.net

  40. EuResist. http://www.euresist.org

  41. Geno2Pheno system. http://www.genafor.org

  42. RDI. http://www.hivrdi.org/

  43. ANRS. http://www.hivfrenchresistance.org/

  44. ANRS. http://pugliese.club.fr/index.htm

  45. HIV-1 Genotypic Drug Resistance rules from REGA Institute. http://www.kuleuven.be/rega/cev/pdf/ResistanceAlgorithm6_22.pdf

  46. The cellML – http://www.cellml.org

  47. The body – complete HIV resource http://bbs.thebody.com/index.html

Download references

Author information

Authors and Affiliations

  1. Natural Computing Research and Applications Group, University College Dublin, Ireland

    Ian Dempsey, Anthony Brabazon & Michael O’Neill

Authors
  1. Ian Dempsey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anthony Brabazon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael O’Neill
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Centre for Quantifiable Quality of Service in Communication Systems (Q2S), Centre of Excellence Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

  2. Department of Computer Science Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Kogalniceanu 1, 400084, Cluj - Napoca, Romania

    Crina Grosan

  3. Department of Electrical & Computer Engineering, University of Alberta, ECERF Bldg., 2nd floor, Edmonton, AB, T6G 2V4, Canada

    Witold Pedrycz

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Dempsey, I., Brabazon, A., O’Neill, M. (2008). A Grammatical Genetic Programming Representation for Radial Basis Function Networks. In: Abraham, A., Grosan, C., Pedrycz, W. (eds) Engineering Evolutionary Intelligent Systems. Studies in Computational Intelligence, vol 82. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75396-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-75396-4_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75395-7

  • Online ISBN: 978-3-540-75396-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation