Vitorino Ramos: The Biological concept of Neoteny in Evolutionary Colour Image Segmentation

35. Vitorino Ramos, The Biological Concept of Neoteny in Evolutionary Colour Image Segmentation - Simple Experiments in Simple Non-Memetic Genetic Algorithms, in Applications of Evolutionary Computation, (Eds.), EvoIASP´01 - 3rd European Workshop on Evolutionary Computation in Image Analysis and Signal Processing [helded by EuroGP´01 - European Conference on Genetic Programming], Lake Como, Milan, Italy, Lecture Notes in Computer Science, Vol. 2037, pp. 364-378, Springer-Verlag, Berlin-Heidelberg, April 18-20, 2001.

Vitorino Ramos - Fast Convergence of Genetic Algorithms through Neoteny

Vitorino Ramos - Fast Convergence of Genetic Algorithms through Neoteny

Figures - Comparison of Convergence between classical Genetic Algorithms and those using Neoteny.

PDF file: paper (740 Kb)

Abstract: Neoteny, also known as 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.

Keywords: Genetic Algorithms, Artificial Neoteny, Dynamic Mutation Rates, Faster Convergence, Colour Image Segmentation, Classification, Clustering.

Cited by:

º Daniel Rivero, R. Vidal, J. Dorado, J. R. Rabuñal, Alejandro Pazos, "Restoration of Old Documents with Genetic Algorithms", in Applications of Evolutionary Computing: EvoWorkshops´03, S. Cagnoni et al (Eds.), Springer Verlag, LNCS, Vol. 2611, pp. 432-443, Essex, UK, April 2003.

º Jarmo T. Alander, "An Indexed Bibliography of Genetic Algorithms in Optics and Image Processing", Department of Electrical Engineering and Automation, University of Vaasa, Finland, March 2006.

Note - The present paper was suggested by Dr. Dobb's Journal on the AI (Artificial Intelligence) Session (June 2001), and by Generation5 (at the forefront of Artificial Intelligence) Web-magazine (Nov. 2001).

Related Works:

63. Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes, submitted to A. Porto, A. Pazos, W. Buno (Eds.), Advancing Artificial Intelligence through Biological Process Applications, IDEA Group Inc., 2007.

70. Ramos, V., Fernandes, C., Rosa, A.C., Abraham, A., Computational Chemotaxis in Ants and Bacteria over Dynamic Environments, submitted to CEC´07 - Congress on Evolutionary Computation, IEEE Press, Singapore, 25-28 Sep. 2007.

69. Fernandes, C., Rosa, A.C., Ramos V., Binary Ant Algorithm, to appear in GECCO´07 - Genetic and Evolutionary Computation Conference, ACM Press, London, UK, 7-11 July, 2007.

55. Vitorino Ramos, Pedro Pina, Exploiting and Evolving Rⁿ Mathematical Morphology Feature Spaces, in Ronse Ch., Najman L., Decencière E. (Eds.), Mathematical Morphology: 40 Years On, pp. 465-474, Springer, Dordrecht, The Netherlands, 2005.

31. Vitorino Ramos, Fernando Muge, 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.), ISBN 3-540-41023-6, Lecture Notes in Computer Science, Vol. 1923, pp. 319-323, Springer-Verlag -Heidelberg, Lisbon, Portugal, 18-20 Sep. 2000.

51. Vitorino Ramos, Ajith Abraham, Evolving a Stigmergic Self-Organized Data-Mining, in ISDA-04, 4th Int. Conf. on Intelligent Systems, Design and Applications, Budapest, Hungary, ISBN 963-7154-30-2, pp. 725-730, August 26-28, 2004.

53. Vitorino Ramos, Jonathan Campbell, John Slater, John Gillespie, Ivan F. Bendezu and Fionn Murtagh, Swarming around Shellfish Larvae Images, in WCLC-05, 2nd World Congress on Lateral Computing, Bangalore, India, 16-18 Dec., 2005.

29. Vitorino Ramos, Filipe Almeida, Artificial Ant Colonies in Digital Image Habitats - A Mass Behaviour Effect Study on Pattern Recognition, Proceedings of ANTS´2000 - 2nd International Workshop on Ant Algorithms (From Ant Colonies to Artificial Ants), Marco Dorigo, Martin Middendorf & Thomas Stüzle (Eds.), pp. 113-116, Brussels, Belgium, 7-9 Sep. 2000.

45. Vitorino Ramos, Ajith Abraham, Swarms on Continuous Data, in CEC´03 - Congress on Evolutionary Computation, IEEE Press, ISBN 078-0378-04-0, pp.1370-1375, Canberra, Australia, 8-12 Dec. 2003.