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Workshops on Applications of Evolutionary Computation

EvoWorkshops 2005: Applications of Evolutionary Computing pp 407–416Cite as

An Implicit Context Representation for Evolving Image Processing Filters

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3449)

Abstract

This paper describes the implementation of a representation for Cartesian Genetic Programming (CGP) in which the specific location of genes within the chromosome has no direct or indirect influence on the phenotype. The mapping between the genotype and phenotype is determined by selforganised binding of the genes, inspired by enzyme biology. This representation has been applied to a version of CGP developed especially for evolution of image processing filters and preliminary results show it outperforms the standard representation in some configurations.

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  • DOI: 10.1007/978-3-540-32003-6_41
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References

  1. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  2. Miller, J., 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)

    CrossRef  Google Scholar 

  3. Miller, J.F., Job, D., Vasilev, V.K.: Principles in the evolutionary design of digital circuits—Part I. Genetic Programming and Evolvable Machines 1, 7–36 (2000)

    MATH  CrossRef  Google Scholar 

  4. Sekanina, L., Drabek, V.: Automatic Design of Image Operators Using Evolvable Hardware. In: Fifth IEEE Design and Diagnostic of Electronic Circuits and Systems, pp. 132–139 (2002)

    Google Scholar 

  5. Sekanina, L.: Image Filter Design with Evolvable Hardware. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, pp. 255–266. Springer, Heidelberg (2002)

    CrossRef  Google Scholar 

  6. Yang, Z., Smith, S.L., Tyrrell, A.M.: Intrinsic Evolvable Hardware in Digital Filter Design. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 389–398. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  7. Yang, Z., Smith, S.L., Tyrrell, A.M.: Digital Circuit Design using Intrinsic Evolvable Hardware. In: Proceedings of 2004 NASA/DoD Conference on Evolvable Hardware, Seattle (2004)

    Google Scholar 

  8. Lones, M.A.: Enzyme Genetic Programming. PhD Thesis, University of York, UK (2003)

    Google Scholar 

  9. Lones, M.A., Tyrrell, A.M.: Enzyme genetic programming. In: Kim, J.-H., Zhang, B.-T., Fogel, G., Kuscu, I. (eds.) Proc. 2001 Congress on Evolutionary Computation, vol. 2, pp. 1183–1190. IEEE Press, Los Alamitos (2001)

    CrossRef  Google Scholar 

  10. Lones, M.A., Tyrrell, A.M.: Crossover and Bloat in the Functionality Model of Enzyme Genetic Programming. In: Proc. Congress on Evolutionary Computation 2002 (CEC 2002), pp. 986–992 (2002)

    Google Scholar 

  11. Lones, M.A., Tyrrell, A.M.: Biomimetic Representation with Enzyme Genetic Programming. Journal of Genetic Programming and Evolvable Machines 3(2), 193–217 (2002)

    MATH  CrossRef  Google Scholar 

  12. Lones, M.A., Tyrrell, A.M.: Modelling biological evolvability: implicit context and variation filtering in enzyme generic programming. BioSystems (2004)

    Google Scholar 

  13. Langdon, W.: Quadratic bloat in genetic programming. In: Whitley, D., Goldberg, D., Cantu-Paz, E. (eds.) Proceedings of the 2000 Genetic and Evolutionary Computation Conference, pp. 451–458 (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics, The University of York, Heslington, York, YO10 5DD, UK

    Stephen L. Smith, Stefan Leggett & Andrew M. Tyrrell

Authors
  1. Stephen L. Smith
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  2. Stefan Leggett
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  3. Andrew M. Tyrrell
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Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. School of Mathematical and Computer Science, Heriot-Watt University, EH14 8AS, Edinburgh, UK

    David Wolfe Corne

  5. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  6. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

  7. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  8. ICIS, Radboud University Nijmegen, The Netherlands

    Elena Marchiori

  9. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  10. School of Computing Sciences, University of East Anglia, UEA Norwich, NR4 7TJ, Norwich, UK

    George D. Smith

  11. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

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Smith, S.L., Leggett, S., Tyrrell, A.M. (2005). An Implicit Context Representation for Evolving Image Processing Filters. In: , et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_41

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  • DOI: https://doi.org/10.1007/978-3-540-32003-6_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25396-9

  • Online ISBN: 978-3-540-32003-6

  • eBook Packages: Computer ScienceComputer Science (R0)