Skip to main content
SpringerLink
Log in
Book cover

Foundations of Computational Intelligence Volume 3 pp 425–460Cite as

Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 203))

Summary

This chapter introduces a class of evolutionary algorithms whose inspiration comes from living processes where cooperation is the main evolutionary strategy. The proposed technique is called Transgenetic Algorithms and is based on two recognized driving forces of evolution: the horizontal gene transfer and the endosymbiosis. These algorithms perform a stochastic search simulating endosymbiotic interactions between a host and a population of endosymbionts. The information exchanging between the host and ensosymbionts is intermediated by agents, called transgenetic vectors, who are inspired on natural mechanisms of horizontal gene transfer. The proposed approach is described and a didactic example with the well-known Traveling Salesman Problem illustrates its basic components. Applications of the proposed technique are reported for two NP-hard combinatorial problems: the Traveling Purchaser Problem and the Bi-objective Minimum Spanning Tree Problem.

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. Aggarwal, V., Aneja, Y., Nair, K.: Minimal spanning tree subject to a side constraint. Computers & Operations Research 9, 287–296 (1982)

    Article  Google Scholar 

  2. Almeida, C.P., Goldbarg, E.F.G., Gonçalves, R.A., Regattieri, M.D., Goldbarg, M.C.: TA-PFP: A transgenetic algorithm to solve the protein folding problem. In: Proceedings of ISDA 2007 Seventh International Conference on Intelligent Systems Design and Applications, vol. 1, pp. 163–168 (2007)

    Google Scholar 

  3. Applegate, D., Bixby, R., Chvatal, V., Cook, W.: Finding tours in the TSP. Technical Report TR99-05. Department of Computational and Applied Mathematics: Rice University (1999)

    Google Scholar 

  4. Barboza, A.O.: Simulação e técnicas da computação evolucionária aplicadas a problemas de programação linear inteira mista. D.Sc. Thesis, Universidade Tecnológica Federal do Paraná, Brazil (2005)

    Google Scholar 

  5. Bazlamaçci, C.F., Hindi, K.S.: Minimum-weight spanning tree algorithms: A survey and empirical study. Computers & Operations Research 28, 767–785 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  6. Bellmore, M., Nemhauser, G.L.: The traveling salesman problem: A survey. Operations Research 16, 538–582 (1968)

    Article  MATH  MathSciNet  Google Scholar 

  7. Boctor, F.F., Laporte, G., Renaud, J.: Heuristics for the traveling purchaser problem. Computers & Operations Research 30, 491–504 (2003)

    Article  MATH  Google Scholar 

  8. Bontoux, B., Feillet, D.: Ant colony optimization for the traveling purchaser problem. Computers & Operations Research 35, 628–637 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  9. Buchner, P.: Endosymbiosis of animals with plant microorganisms. Wiley Interscience, New York (1965)

    Google Scholar 

  10. Bull, L., Fogarty, T.C.: Artificial symbiogenesis. Artificial Life 2, 269–292 (1995)

    Article  Google Scholar 

  11. Chan, T.-M., Man, K.-F., Tang, K.-S., Kwong, S.A.: Jumping gene algorithm for multiobjective resource management in wideband CDMA. The Computer Journal 48(6), 749–768 (2005)

    Article  Google Scholar 

  12. Chen, I., Dubnau, D.: DNA uptake during bacterial transformation. Nature Reviews Microbiology 2, 241–249 (2004)

    Article  Google Scholar 

  13. Conover, W.J.: Practical nonparametric statistics, 3rd edn. John Wiley & Sons, Chichester (2001)

    Google Scholar 

  14. Daida, J.M., Grasso, C.S., Stanhope, S.A., Ross, S.J.: Symbionticism and complex adaptive systems I: Implications of having symbiosis occur in nature. In: Proceedings of the Fifth Annual Conference on Evolutionary Programming, pp. 177–186 (1996)

    Google Scholar 

  15. Doolittle, W.F.: Lateral genomics. Trends in Genetics 15(12), M5–M8 (1999)

    Article  Google Scholar 

  16. Dutta, C., Pan, A.: Horizontal gene transfer and bacterial diversity. Journal of Biosciences 27, 27–33 (2002)

    Article  Google Scholar 

  17. Eigen, M., Schuster, P.: The hypercycle: A principle of natural selforganization. Naturwissenschafter 64(11), 541–565 (1977)

    Article  Google Scholar 

  18. Fukuda, T., Kubota, N., Shimojima, K.: Virus-evolutionary genetic algorithm and its application to traveling salesmam problem. In: Yao, X. (ed.) Evolutionary Computation, Theory and Applications. World Scientific, Singapore (1999)

    Google Scholar 

  19. Garey, M.R., Johnson, D.S.: Computers and intractability: A guide to the theory of NP-completeness. Freeman, New York (1979)

    MATH  Google Scholar 

  20. Goldbarg, E.F.G., Castro, M.P., Goldbarg, M.C.: A transgenetic algorithm for the gas network pipe sizing problem. Computational Methods 1, 893–904 (2006)

    Google Scholar 

  21. Goldbarg, E.F.G., Goldbarg, M.C., Bagi, L.B.: Transgenetic algorithm: A new evolutionary perspective for heuristics design. In: Proceedings of GECCO 2007 Genetic and Evolutionary Computation Conference, pp. 2701–2708 (2007)

    Google Scholar 

  22. Goldbarg, E.F.G., Goldbarg, M.C., Costa, W.E.: Evolutionary algorithms applied to the workover rigs schedule problem. Annals of XI Latin-Iberian American Congress of Operations Research (2002)

    Google Scholar 

  23. Goldbarg, M.C., Goldbarg, E.F.G., Medeiros Neto, F.D.: Algoritmos evolucionários na determinação da configuração de custo mínimo de sistemas de co-geração de energia com base no á natural. Pesquisa Operacional 25(2), 231–259 (2005)

    Article  Google Scholar 

  24. Gouvêa, E.F.: Transgenética computacional: Um estudo algorítmico. Ph.D. Thesis, Universidade Federal do Rio de Janeiro, Brazil (2001)

    Google Scholar 

  25. Guttin, G., Punnen, A.: The traveling salesman problem and its variations. Kluwer Academic Publishers, Dordrecht (2002)

    Google Scholar 

  26. Hansen, M.P., Jaszkiewicz, A.: Evaluating the quality of approximations to the non-dominated set. Technical Report IMM-REP-1998-7, Technical University, Denmark (1998)

    Google Scholar 

  27. Harvey, I.: The microbial genetic algorithm (unpublished manuscript) (1996), http://citeseer.ist.psu.edu/13824.html

  28. Hillis, D.W.: Co-evolving parasites improve simulated evolution in an optimization procedure. Physica D 42, 228–234 (1999)

    Article  Google Scholar 

  29. Jain, R., Rivera, M.C., Lake, J.A.: Horizontal gene transfer among genomes: The complexity hypothesis. Proceedings of the National Academy of Sciences USA 96, 3801–3806 (1999)

    Article  Google Scholar 

  30. Jain, R., Rivera, M.C., Moore, J.E., Lake, J.A.: Horizontal gene transfer accelerates genome innovation and evolution. Molecular Biology and Evolution 20(10), 1598–1602 (2003)

    Article  Google Scholar 

  31. Kim, J.Y., Kim, Y., Kim, Y.K.: An endosymbiotic evolutionary algorithm for optimization. Applied Intelligence 15, 117–130 (2001)

    Article  MATH  Google Scholar 

  32. Kim, Y.K., Kim, J.Y., Kim, Y.: An endosymbiotic evolutionary algorithm for the integration of balancing and sequencing in mixed-model U-lines. European Journal of Operational Research 168, 838–852 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  33. Knowles, J.D.: Local-search and hybrid evolutionary algorithms for Pareto optimization, PhD Thesis, Department of Computer Science, University of Reading, Reading, UK (2002)

    Google Scholar 

  34. Knowles, J.D., Corne, D.W.: A comparison of encodings and algorithms for multiobjective spanning tree problems. In: Proceedings of the 2001 Congress on Evolutionary Computation (CEC 2001), pp. 544–551 (2001)

    Google Scholar 

  35. Knowles, J.D., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers, TIK 214, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich (2006)

    Google Scholar 

  36. Kubota, N., Arakawa, T., Fukuda, T., Shimojima, K.: Trajectory generation for redundant manipulator using virus evolutionary genetic algorithm. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 205–210 (1997)

    Google Scholar 

  37. Kubota, N., Shimojima, K., Fukuda, T.: Virus-evolutionary genetic algorithm - coevolution of planar grid model. In: Proceedings of the Fifth IEEE International Conference on Fuzzy Systems (FUZZIEEE 1996), vol. 1, pp. 8–11 (1996)

    Google Scholar 

  38. Kutschera, U., Niklas, K.J.: Endosymbiosis, cell evolution, and speciation. Theory in Biosciences 124, 1–24 (2005)

    Article  Google Scholar 

  39. Leite, L.E., Souza Fillho, G., Goldbarg, M.C., Goldbarg, E.F.G.: Comparando algoritmos genéticos e transgenéticos para otimizar a configuração de um serviço de distribuição de Vídeo baseado em replicação móvel. Anais do SBRC2004 22 Simpósio Brasileiro de Redes de Computadores 1, 129–132 (2004)

    Google Scholar 

  40. Margulis, L.: Symbiosis in cell evolution: Microbial communities in the archean and proterozoic eons. W.H. Freeman, New York (1992)

    Google Scholar 

  41. Margulis, L.: Serial endosymbiotic theory (SET) and composite individuality. Microbiology Today 31, 172–174 (2004)

    Google Scholar 

  42. Margulis, L., Sagan, D.: Microcosmos. Summit Books, New York (1986)

    Google Scholar 

  43. Maynard-Smith, J., Szathmáry, E.: The major transitions in evolution. W.H. Freeman, Oxford (1995)

    Google Scholar 

  44. Michalewicz, Z., Fogel, D.B.: How to solve it: Modern heuristics. Springer, Heidelberg (2000)

    MATH  Google Scholar 

  45. Mitchell, M.: An introduction to genetic algorithms. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  46. Mühlenbein, H., Voigt, H.-M.: Gene pool recombination in genetic algorithms. In: Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 104–113 (1995)

    Google Scholar 

  47. Novozhilov, A.S., Karev, G.P., Koonin, E.V.: Mathematical modeling of evolution of horizontally transferred genes. Molecular Biology and Evolution 22(8), 1721–1732 (2005)

    Article  Google Scholar 

  48. Perales-Graván, C., Lahoz-Beltra, R.: An AM radio receiver designed with a genetic algorithm based on a bacterial conjugation genetic operator. IEEE Transactions on Evolutionary Computation 12(2), 1–29 (2008)

    Article  Google Scholar 

  49. Ramos, I.C.O., Goldbarg, M.C., Goldbarg, E.F.G., Dória Neto, A.D.: Logistic regression for parameter tuning on an evolutionary algorithm. In: Proceedings of the IEEE CEC 2005 Congress on Evolutionary Computation, vol. 2, pp. 1061–1068 (2005)

    Google Scholar 

  50. Riera-Ledesma, J., Salazar-González, J.J.: A heuristic approach for the traveling purchaser problem. European Journal of Operational Research 162, 142–152 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  51. Rocha, D.A.M., Goldbarg, E.F.G., Goldbarg, M.C.: A memetic algorithm for the biobjective minimum spanning tree problem. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2006. LNCS, vol. 3906, pp. 222–233. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  52. Rocha, D.A.M., Goldbarg, E.F.G., Goldbarg, M.C.: A new evolutionary algorithm for the bi-objective minimum spanning tree. In: Proceedings of ISDA 2007 Seventh International Conference on Intelligent Systems Design and Applications, pp. 735–740 (2007)

    Google Scholar 

  53. Rosenkrantz, D.J., Stearns, R.E., Lewis II, P.M.: An analysis of several heuristics for the traveling salesman problem. SIAM Journal on Computing 6, 563–581 (1977)

    Article  MATH  MathSciNet  Google Scholar 

  54. Rosin, C.D., Belew, R.K.: New methods for competitive coevolution. Evolutionary Computation 5(1), 1–29 (1997)

    Article  Google Scholar 

  55. Schmidt, C., Goldbarg, E.F.G., Goldbarg, M.C.: A hybrid transgenetic algorithm for the prize collecting Steiner tree problem. In: Proceedings of ISDA 2007 Seventh International Conference on Intelligent Systems Design and Applications, vol. 1, pp. 271–276 (2007)

    Google Scholar 

  56. Shapiro, J.A.: Transposable elements as the key to a 21st century view of evolution. Genetica 107, 171–179 (1999)

    Article  Google Scholar 

  57. Simões, A.B., Costa, E.: Transposition: A biologically inspired mechanism to use with genetic algorithms. In: Proceedings of the Fourth International Conference of Neural Networks and Genetic Algorithms, pp. 178–186 (1999)

    Google Scholar 

  58. Simões, A.B., Costa, E.: Transposition versus crossover: An empirical study. In: Proceedings of the Genetic and Evolutionary Compuation Conference (GECCO 1999), pp. 612–619 (1999)

    Google Scholar 

  59. Simões, A.B., Costa, E.: On biologically inspired genetic operators: Transformation in the standard genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 584–591 (2001)

    Google Scholar 

  60. Simões, A.B., Costa, E.: An evolutionary approach to the zero/one knapsack problem: Testing ideas from biology. In: Proceedings of the Fifth International Conference on Neural Networks and Genetic Algorithms (ICANNGA 2001), pp. 22–25 (2001)

    Google Scholar 

  61. Smith, P.W.H.: Finding hard satisfiability problems using bacterial conjugation. In: Proceedings of the AISB96 Workshop on Evolutionary Computing, pp. 236–244 (1996)

    Google Scholar 

  62. Theissen, U., Martin, W.: The difference between organelles and endosymbionts. Current Biology 16(24), R1016–R1017 (2006)

    Article  Google Scholar 

  63. Timmis, J.N., Ayliffe, M.A., Huang, C.Y., Martin, W.: Endosymbiotic gene transfer: organelle genomes forge eukaryotic chromosomes. Nature Reviews Genetic 5, 123–135 (2004)

    Article  Google Scholar 

  64. Vothknecht, U.C., Soll, J.: Protein import: The hitchhikers guide into chloroplasts. Biological Chemistry 381, 887–897 (2000)

    Article  Google Scholar 

  65. Wernegreen, J.J.: For better or worse: genomic consequences of intracellular mutualism and parasitism. Genetics & Development 15, 572–583 (2005)

    Article  Google Scholar 

  66. Yeung, S.-H., Ng, H.-K., Man, K.-F.: Multi-criteria design methodology of a dielectric resonator antenna with jumping genes evolutionary algorithm. International Journal of Electronics and Communication (AEÜ) 62, 266–276 (2008)

    Google Scholar 

  67. Zaneveld, J.R., Nemergut, D.R., Knight, R.: Are all horizontal gene transfers created equal? Prospects for mechanism-based studies of HGT patterns. Microbiology 154, 1–15 (2008)

    Article  Google Scholar 

  68. Zhou, G., Gen, M.: Genetic algorithm approach on multi-criteria minimum spanning tree problem. European Journal of Operational Research 114, 141–152 (1999)

    Article  MATH  Google Scholar 

  69. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)

    Article  Google Scholar 

  70. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Fonseca, V.G.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidade Federal do Rio Grande do Norte, Campus Universitário Lagoa Nova, Natal, Brazil

    Elizabeth F. Gouvêa Goldbarg & Marco C. Goldbarg

Authors
  1. Elizabeth F. Gouvêa Goldbarg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco C. Goldbarg
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  2. College of Business Administration Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Université Paris XII Fac. Sciences, LERISS, 61 avenue du Général de Gaulle Building P2 - Room 350, 94010, Créteil, France

    Patrick Siarry

  4. Department of Computer Science, University of Pretoria, 0002, Pretoria, South Africa

    Andries Engelbrecht

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Goldbarg, E.F.G., Goldbarg, M.C. (2009). Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms. In: Abraham, A., Hassanien, AE., Siarry, P., Engelbrecht, A. (eds) Foundations of Computational Intelligence Volume 3. Studies in Computational Intelligence, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01085-9_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01085-9_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01084-2

  • Online ISBN: 978-3-642-01085-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation