The Biological Concept of Neoteny in Evolutionary Color Image Segmentation - Simple Experiments in Simple Non-memetic Genetic Algorithms

  • Vitorino Ramos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)


Neoteny, also spelled Paedomorphosis, can be defined in biological terms as the retention by an organism of juvenile or even larval traits into later life. In some species, all morphological development is retarded; the organism is juvenilized but sexually mature. Such shifts of reproductive capability would appear to have adaptive significance to organisms that exhibit it. In terms of evolutionary theory, the process of paedomorphosis suggests that larval stages and developmental phases of existing organisms may give rise, under certain circumstances, to wholly new organisms. Although the present work does not pretend to model or simulate the biological details of such a concept in any way, these ideas were incorporated by a rather simple abstract computational strategy, in order to allow (if possible) for faster convergence into simple non-memetic Genetic Algorithms, i.e. without using local improvement procedures (e.g. via Baldwin or Lamarckian learning). As a case-study, the Genetic Algorithm was used for colour image segmentation purposes by using K-mean unsupervised clustering methods, namely for guiding the evolutionary algorithm in his search for finding the optimal or sub-optimal data partition. Average results suggest that the use of neotonic strategies by employing juvenile genotypes into the later generations and the use of linear-dynamic mutation rates instead of constant, can increase fitness values by 58% comparing to classical Genetic Algorithms, independently from the starting population characteristics on the search space.


Genetic Algorithm Mutation Rate Image Segmentation Biological Concept Simple Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Dawkins, Richard, 1976, The Selfish Gene, Oxford University Press, Oxford.Google Scholar
  2. 2.
    Ramos, V., “Artificial Neoteny in Evolutionary Image Segmentation”, Proc. of SIARP'2000-5th IberoAmerican Symp. on Pattern Recognition, F. Muge, M. Piedade & R. Caldas Pinto (Eds.), ISBN 972-97711-1-1, pp. 69–78, Lisbon, Portugal, 11-13 Sep. 2000.Google Scholar
  3. 3.
    Davis, L.D., 1991, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New-York.Google Scholar
  4. 4.
    Goldberg, D.E., 1989, Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-Wesley Reading, Massachusetts.zbMATHGoogle Scholar
  5. 5.
    Michalewicz, Z., 1996, Genetic Algorithms + Data Structures = Evolution Programs, 3rd Ed., Springer-Verlag.Google Scholar
  6. 6.
    Duda, R.O. and Hart, P.E., 1973, Pattern Classification and Scene Analysis, John Wiley & Sons, New-York.zbMATHGoogle Scholar
  7. 7.
    Bhanu, B. and Lee, S., 1994, Genetic Learning for Adaptive Image Segmentation, Kluwer Acad. Press.Google Scholar
  8. 8.
    Pal, S.K. and Wang P.P. (Eds.), 1996, Genetic Algorithms for Pattern Recognition, CRC Press.Google Scholar
  9. 9.
    Stern, J.M., 1992, “Simulated annealing with a temperature dependent penalty function”, ORSA Journal on Computing, vol. 4, pp. 311–319.CrossRefzbMATHGoogle Scholar
  10. 10.
    Cagnoni, S., Dobrzeniecki, A.B., Poli, R. and Yanch, J.C., 1999, “Genetic Algorithm-based Interactive Segmentation of 3D Medical Images”,Image and Vision Computing 17, pp. 881–895.CrossRefGoogle Scholar
  11. 11.
    Andrey, P., 1999, “Selectionist Relaxation: Genetic Algorithms applied to Image Segmentation”, Image and Vision Computing 17, pp. 175–187.CrossRefGoogle Scholar
  12. 12.
    Ramos V., Almeida F., 2000, “Artificial Ant Colonies in Digital Image Habitats-A Mass Behaviour Effect Study on Pattern Recognition”, Proceedings of ANTS’2000-2nd Inter. Workshop on Ant Algorithms (From Ant Colonies to Artificial Ants), M. Dorigo, M. Middendorf & T. Stüzle (Eds.), pp. 113–116, Brussels, Belgium, Sep. 7-9.Google Scholar
  13. 13.
    Bounsaythip, C. and Alander J.T., 1997, “Genetic Algorithms in Image Processing-A Review”, Proc. of the 3 rd Nordic Workshop on Genetic Algorithms and their Applications, Metsatalo, Univ. of Helsinki, Helsinki, Finland, pp. 173–192.Google Scholar
  14. 14.
    Zhang, Y.J, 1996, “A Survey on Evaluating Methods for Image Segmentation”, Pattern Recognition 29(8), pp. 1335–1246.CrossRefGoogle Scholar
  15. 15.
    Ramos V., Muge F., 2000, “On Image Filtering, Noise and Morphological Size Intensity Diagrams”, RecPad’2000-11th Portuguese Conf. on Pattern Recognition, in A.C. Campilho and A.M. Mendonça (Eds.), ISBN 972-96883-2-5, pp. 483–491, Porto, Portugal, May 11-12.Google Scholar
  16. 16.
    Falkenauer, E., 1998, Genetic Algorithms and Grouping Problems, John Wiley & Sons, Boston.zbMATHGoogle Scholar
  17. 17.
    Ramos, V., 1997, Evolution and Cognition in Image Analysis, MSc Thesis dissert. (in Portuguese), 230 pp., Instituto Superior Técnico-IST, Lisbon, Portugal, December.Google Scholar
  18. 18.
    Ramos V., Muge F., 2000, “Map Segmentation by Colour Cube Genetic K-Mean Clustering”, Proc. of ECDL’2000-4th European Conference on Research and Advanced Technology for Digital Libraries, J. Borbinha and T. Baker (Eds.), Lecture Notes in Computer Science, Vol. 1923, pp. 319–323, Springer-Verlag, Heidelberg.CrossRefGoogle Scholar
  19. 19.
    Rudolph, G., 1994, “Convergence Analysis of Canonical Genetic Algorithms”, IEEE Trans. on Neural Networks, special issue on EP.Google Scholar
  20. 20.
    Davis, T.E., 1991, Toward an Extrapolation of the Simulated Annealing Convergence Theory onto the Simple Genetic Algorithm, PhD dissert., Gainesville: University of Florida.Google Scholar
  21. 21.
    Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E., 1953; “Equation of State Calculations by Fast Computing Machines”,J. Chem. Phys., vol. 21,n.6, pp. 1087–1092.CrossRefGoogle Scholar
  22. 22.
    Ingber, Lester, 1989, “Very Fast Re-Annealing”, J. Mathl. Comput. Modelling, v. 12, pp. 967–973.MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Ingber, Lester, 1993, “Simulated Annealing: Practice versus Theory”, J. Mathl. Comput. Modelling, v. 18,n. 11, pp. 29–57.MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Ingber, Lester, 1995, “Adaptive Simulated Annealing (ASA): Lessons Learned”, invited paper, Special issue (Simulated Annealing Applied to Combinatorial Optimization) of Control and Cybernetics.Google Scholar
  25. 25.
    Goldstein, J., 1988, “Mean Square Rates of Convergence in the Continuous Time Simulated Annealing Algorithm on R d”, ADVAM: Adv. in Applied Mathematics, vol. 9.Google Scholar
  26. 26.
    Rajasekaran, S., 1990, “On the Convergence Time of Simulated Annealing”, Computer and Information Science, University of Pennsylvania.Google Scholar
  27. 27.
    Yoshimura, M. and Oe, S., 1999, “Evolutionary Segmentation of Texture Image using Genetic Algorithms towards Automatic Decision of Optimum Number of Segmentation Areas”, Pattern Recognition 32, pp. 2041–2054.CrossRefGoogle Scholar
  28. 28.
    Bäck, Th., 1992, “The Interaction of Mutation Rate, Selection, and Self-Adaptation within a Genetic Algorithm”, in, Männer, R. and Manderick, B. (Eds.), Parallel Problem Solving from Nature,2, pp. 85–94, Elsevier, Amsterdam.Google Scholar
  29. 29.
    Bäck, Th., Schwefel, H.-P., 1993, “An Overview of Evolutionary Algorithms for Parameter Optimization”, Evolutionary Computation, 1(1), pp. 1–23.CrossRefGoogle Scholar
  30. 30.
    Bäck, Th., Schütz, M., 1996, “Intelligent Mutation Rate Control in Canonical Genetic Algorithms”, in, Ras, W. and Michalewicz, M. (Eds.): Foundation of Intelligent Systems-9th Int. Syposium, ISMIS’96, pp. 158–167, Springer, Berlin.Google Scholar
  31. 31.
    Mühlenbein, H., 1992, “How Genetic Algorithms Really Work: I. Mutation and Hillclimbing”, in, Männer, R. and Manderick, B. (Eds.), Parallel Problem Solving from Nature, 2, pp. 15–25, Elsevier, Amsterdam.Google Scholar
  32. 32.
    Schwefel, H.-P., 1981, Numerical Optimization of Computer Models, Chichester: Wiley.zbMATHGoogle Scholar
  33. 33.
    Fogel, D.B., 1995, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, Piscataway, NJ.zbMATHGoogle Scholar
  34. 34.
    Serra, J., 1982, Image Analysis and Mathematical Morphology, Academic Press, London.zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Vitorino Ramos
    • 1
  1. 1.CVRM - GeoSystems Centre, Technical Univ. of Lisbon (IST)LisboaPortugal

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