Behavior-Finding: Morphogenetic Designs Shaped by Function

  • Daniel Lobo
  • Jose David Fernández
  • Francisco J. Vico
Part of the Understanding Complex Systems book series (UCS)


Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding.


Mutation Operator Molecular Motor Boolean Network Docking Site Catalytic Core 
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.


  1. 1.
    Andersen, T., Newman, R., Otter, T.: Shape homeostasis in virtual embryos. Artif. Life 15(2), 161–183 (2009)CrossRefGoogle Scholar
  2. 2.
    Basanta, D., Miodownik, M., Baum, B.: The evolution of robust development and homeostasis in artificial organisms. PLoS Comput. Biol. 4(3), e1000,030 (2008)Google Scholar
  3. 3.
    Bentley, P., Kumar, S.: Three ways to grow designs: a comparison of embryogenies for an evolutionary design problem. In: Proceedings Genetic Evolutionary Computation Conference (GECCO), vol. 1, pp. 35–43. Morgan Kaufmann (1999)Google Scholar
  4. 4.
    Bongard, J.C., Pfeifer, R.: Repeated structure and dissociation of genotypic and phenotypic complexity in artificial ontogeny. In: Proceedings Genetic Evolutionary Computation Conference (GECCO), pp. 829–836. Morgan Kaufmann (2001)Google Scholar
  5. 5.
    Bongard, J.C., Pfeifer, R.: Evolving complete agents using artificial ontogeny. In: Hara, F., Pfeifer, R. (eds.) Morpho-functional Machines: The New Species, pp. 237–258. Springer (2003)Google Scholar
  6. 6.
    Chavoya, A., Duthen, Y.: A cell pattern generation model based on an extended artificial regulatory network. Biosystems 94(1–2), 95–101 (2008)CrossRefGoogle Scholar
  7. 7.
    Coates, P., Broughton, T., Jackson, H.: Exploring three-dimensional design worlds using lindenmayer systems and genetic programming. In: Bentley, P.J. (ed.) Evolutionary Design by Computers, pp. 323–341. Morgan Kaufmann (1999)Google Scholar
  8. 8.
    Davidich, M., Bornholdt, S.: The transition from differential equations to boolean networks: a case study in simplifying a regulatory network model. J. Theor. Biol. 255(3), 269–277 (2008)CrossRefGoogle Scholar
  9. 9.
    Davidson, E.H.: The Regulatory Genome: Gene Regulatory Networks in Development and Evolution, 1 edn. Academic Press, London (2006)Google Scholar
  10. 10.
    Davidson, E.H., Erwin, D.H.: Gene regulatory networks and the evolution of animal body plans. Science 311(5762), 796–800 (2006)CrossRefGoogle Scholar
  11. 11.
    Dellaert, F., Beer, R.D.: A developmental model for the evolution of complete autonomous agents. In: Proceedings from Animals to Animats: International Conference on Simulation of Adaptive Behavior (ISAB), pp. 393–401. MIT Press (1996)Google Scholar
  12. 12.
    Devert, A., Bredeche, N., Schoenauer, M.: Robust multi-cellular developmental design. In: Proceedings Genetic Evolutionary Computation Conference (GECCO), pp. 982–989. ACM (2007)Google Scholar
  13. 13.
    Devert, A., Bredeche, N., Schoenauer, M.: Unsupervised learning of echo state networks: a case study in artificial embryogeny. In: Proceedings of International Conference on Artificial Evolution (EA). Springer (2008)Google Scholar
  14. 14.
    de Garis, H.: Genetic programming: artificial nervous systems, artificial embryos and embryological electronics. In: Proceedings of Parallel Problem Solving Nature (PPSN). Springer (1991)Google Scholar
  15. 15.
    de Garis, H.: Artificial embryology: the genetic programming of cellular differentiation. In: Proceedings of the III Workshop in Artificial Life, Santa Fe, New Mexico. Addison-Wesley (1992)Google Scholar
  16. 16.
    de Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1), 67–103 (2002)CrossRefGoogle Scholar
  17. 17.
    Eggenberger, P.: Cell interactions as a control tool of developmental processes for evolutionary robotics. In: Proceedings from Animals to Animats: International Conference Simulation Adaptive Behavior (ISAB), pp. 440–448. MIT Press (1996)Google Scholar
  18. 18.
    Eggenberger, P.: Evolving morphologies of simulated 3D organisms based on differential gene expression. In: Proceedings of European Conference on Artificial Life (ECAL), pp. 205–213. MIT Press (1997)Google Scholar
  19. 19.
    Eggenberger, P.: Genome-physics interaction as a new concept to reduce the number of genetic parameters in artificial evolution. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 191–198. IEEE-Press (2003)Google Scholar
  20. 20.
    Federici, D., Downing, K.: Evolution and development of a multicellular organism: scalability, resilience, and neutral complexification. Artif. Life 12(3), 381–409 (2006)CrossRefGoogle Scholar
  21. 21.
    Fernández, J.D., Vico, F.J.: Automating the search of molecular motor templates by evolutionary methods. Biosystems 106, 82–93 (2011)CrossRefGoogle Scholar
  22. 22.
    Fernández-Blanco, E., Dorado, J., Rabuñal, J.R., Gestal, M., Pedreira, N.: A new evolutionary computation technique for 2D morphogenesis and information processing. WSEAS Trans. Inf. Sci. Appl. 4(3), 600–607 (2007)Google Scholar
  23. 23.
    Floreano, D., Keller, L.: Evolution of adaptive behaviour in robots by means of Darwinian selection. PLoS Biol. 8(1), e1000,292 (2010)Google Scholar
  24. 24.
    Gruau, F.: Genetic micro programming of neural networks. In: Kinnear, K.E. (ed.) Advances in Genetic Programming, pp. 495–518. MIT Press (1994)Google Scholar
  25. 25.
    Haddow, P.C., Hoye, J.: Achieving a simple development model for 3D shapes: are chemicals necessary? In: Proceedings of Genetic Evolutionary Computation Conference (GECCO), pp. 1013–1020. ACM (2007)Google Scholar
  26. 26.
    Hemberg, M., O’Reilly, U.M.: Integrating generative growth and evolutionary computation for form exploration. Genet. Program. Evol. Mach. 8(2), 163–186 (2007)CrossRefGoogle Scholar
  27. 27.
    Hogeweg, P.: Evolving mechanisms of morphogenesis: on the interplay between differential adhesion and cell differentiation. J. Theor. Biol. 203(4), 317–333 (2000)CrossRefGoogle Scholar
  28. 28.
    Hornby, G.S.: Functional scalability through generative representations: the evolution of table designs. Environ. Plan. B 31(4), 569–587 (2004)CrossRefGoogle Scholar
  29. 29.
    Hornby, G.S., Lipson, H., Pollack, J.B.: Evolution of generative design systems for modular physical robots. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), vol. 4, pp. 4146–4151. IEEE-Press (2001)Google Scholar
  30. 30.
    Hornby, G.S., Lipson, H., Pollack, J.B.: Generative representations for the automated design of modular physical robots. IEEE Trans. Robot. Autom. 19(4), 703–719 (2003)CrossRefGoogle Scholar
  31. 31.
    Hornby, G.S., Pollack, J.B.: Creating high-level components with a generative representation for body-brain evolution. Artif. Life 8(3), 223–246 (2002)CrossRefGoogle Scholar
  32. 32.
    Joachimczak, M., Wróbel, B.: Evo-devo in silico: a model of a gene network regulating multicellular development in 3D space with artificial physics. In: Proc. International Conference on the Simulation and Synthesis of Living Systems (Artificial Life XI), pp. 297–304. MIT Press (2008)Google Scholar
  33. 33.
    Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)CrossRefGoogle Scholar
  34. 34.
    Kniemeyer, O., Buck-Sorlin, G.H., Kurth, W.: A graph grammar approach to artificial life. Artif. Life 10(4), 413–431 (2004)Google Scholar
  35. 35.
    Komosinski, M., Rotaru-Varga, A.: Comparison of different genotype encodings for simulated 3D agents. Artif. Life 7(4), 395–418 (2002)CrossRefGoogle Scholar
  36. 36.
    Kowaliw, T., Grogono, P., Kharma, N.: The evolution of structural design through artificial embryogeny. In: IEEE Symposium on Artificial Life (IEEE-ALIFE), pp. 425–432 (2007)Google Scholar
  37. 37.
    Koza, J.R.: Gene duplication to enable genetic programming to concurrently evolve both the architecture and work-performing steps of a computer program. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), vol. 1, pp. 734–740. Morgan Kaufmann (1995)Google Scholar
  38. 38.
    Kumar, S., Bentley, P.J.: Implicit evolvability: an investigation into the evolvability of an embryogeny. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO). Morgan Kaufmann (2000)Google Scholar
  39. 39.
    Kumar, S., Bentley, P.J.: Biologically inspired evolutionary development. In: Proceedings of International Conference Evolvable Systems (ICES), pp. 99–106. Springer (2003)Google Scholar
  40. 40.
    Kundu, S., Sorensen, D.C., Phillips Jr, G.N.: Automatic domain decomposition of proteins by a gaussian network model. Proteins Struct. Funct. Bioinf. 57(4), 725–733 (2004)CrossRefGoogle Scholar
  41. 41.
    Levine, M., Tjian, R.: Transcription regulation and animal diversity. Nature 424(6945), 147–151 (2003)CrossRefGoogle Scholar
  42. 42.
    Lister, I., Schmitz, S., Walker, M., Trinick, J., Buss, F., Veigel, C., Kendrick-Jones, J.: A monomeric myosin VI with a large working stroke. EMBO J. 23(8), 1729–1738 (2004)CrossRefGoogle Scholar
  43. 43.
    Lobo, D., Hjelle, D.A., Lipson, H.: Reconfiguration algorithms for robotically manipulatable structures. In: Proceedings of ASME/IFToMM International Conference on Reconfigurable Mechanisms Robots (ReMAR), pp. 13–22. IEEE-Press (2009)Google Scholar
  44. 44.
    Lobo, D., Vico, F.J.: Evolution of form and function in a model of differentiated multicellular organisms with gene regulatory networks. Biosystems 102(2–3), 112–123 (2010)CrossRefGoogle Scholar
  45. 45.
    Lobo, D., Vico, F.J.: Evolutionary development of tensegrity structures. Biosystems 101(3), 167–176 (2010)CrossRefGoogle Scholar
  46. 46.
    Lobo, D., Vico, F.J., Dassow, J.: Graph grammars with string-regulated rewriting. Theor. Comput. Sci. 412(43), 6101–6111 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  47. 47.
    Lu, M.: The role of shape in determining molecular motions. Biophys. J. 89(4), 2395–2401 (2005)CrossRefGoogle Scholar
  48. 48.
    Matsushita, K., Lungarella, M., Paul, C., Yokoi, H.: Locomoting with less computation but more morphology. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 2008–2013. IEEE-Press (2005)Google Scholar
  49. 49.
    Miller, J.F.: Evolving a self-repairing, self-regulating, french flag organism. In: Proceedings Genetic and Evolutionary Computation Conference (GECCO), pp. 129–139. Springer (2004)Google Scholar
  50. 50.
    Motro, R.: Tensegrity: Structural Systems for the Future. Butterworth-Heinemann, Oxford (2006)Google Scholar
  51. 51.
    O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Trans. Evolut. Comput. 5(4), 349–358 (2001)Google Scholar
  52. 52.
    O’Neill, M., Swafford, J.M., McDermott, J., Byrne, J., Brabazon, A., Shotton, E., McNally, C., Hemberg, M.: Shape grammars and grammatical evolution for evolutionary design. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp. 1035–1042. ACM (2009)Google Scholar
  53. 53.
    Paul, C.: Morphological computation: a basis for the analysis of morphology and control requirements. Robot. Auton. Syst. 54(8), 619–630 (2006)CrossRefGoogle Scholar
  54. 54.
    Paul, C., Lipson, H., Valero-Cuevas, F.: Evolutionary form-finding of tensegrity structures. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp. 3–10. ACM (2005)Google Scholar
  55. 55.
    Pfeifer, R., Iida, F., Gomez, G.: Morphological computation for adaptive behavior and cognition. Int. Congr. Ser. 1291, 22–29 (2006)CrossRefGoogle Scholar
  56. 56.
    Pollack, J., Lipson, H., Hornby, G., Funes, P.: Three generations of automatically designed robots. Artif. Life 7, 215–223 (2001)CrossRefGoogle Scholar
  57. 57.
    Prusinkiewicz, P., Lindenmayer, A.: The Algorithmic Beauty of Plants. Springer, New York (1990)Google Scholar
  58. 58.
    Reil, T.: Dynamics of gene expression in an artificial genome—implications for biological and artificial ontogeny. In: Proceedings of the European Conference on Artificial Life (ECAL), pp. 457–466. Springer (1999)Google Scholar
  59. 59.
    Rieffel, J., Pollack, J.: The emergence of ontogenic scaffolding in a stochastic development environment. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp. 804–815. Springer (2004)Google Scholar
  60. 60.
    Rieffel, J., Pollack, J.: Crossing the fabrication gap: evolving assembly plans to build 3D objects. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC). IEEE-Press (2006)Google Scholar
  61. 61.
    Roggen, D., Federici, D.: Multi-cellular development: is there scalability and robustness to gain? In: Proceedings of the Parallel Problem Solving Nature (PPSN), pp. 391–400. Springer (2004)Google Scholar
  62. 62.
    Roggen, D., Federici, D., Floreano, D.: Evolutionary morphogenesis for multi-cellular systems. Genet. Program. Evol. Mach. 8(1), 61–96 (2007)CrossRefGoogle Scholar
  63. 63.
    Roggen, D., Floreano, D., Mattiussi, C.: A morphogenetic evolutionary system: phylogenesis of the poetic circuit. In: Proceedings of International Conference on Evolvable Systems (ICES), pp. 153–164. Springer (2003)Google Scholar
  64. 64.
    Rosenman, M.A.: The generation of form using an evolutionary approach. In: Dasgupta, D., Michalewicz, Z. (eds.) Evolutionary Algorithms in Engineering Applications, pp. 69–86. Springer (1997)Google Scholar
  65. 65.
    Rudolph, S., Alber, R.: An evolutionary approach to the inverse problem in rule-based design representations. In: Proceedings of International Conference on Artificial Intelligence in Design (AID). Kluwer Publishers (2002)Google Scholar
  66. 66.
    Schliwa, M., Woehlke, G.: Molecular motors. Nature 422(6933), 759–765 (2003)CrossRefGoogle Scholar
  67. 67.
    Schnier, T., Gero, J.: Learning genetic representations as alternative to hand-coded shape grammars. In: Proceedings of International Conference on Artificial Intelligence in Design (AID). Kluwer Publishers (1996)Google Scholar
  68. 68.
    Schot, S.H.: Jerk: the time rate of change of acceleration. Am. J. Phys. 46(11), 1090–1094 (1978)CrossRefGoogle Scholar
  69. 69.
    Shea, K., Cagan, J.: Innovative dome design: applying geodesic patterns with shape annealing. Artif. Int. Eng. Des. Anal. Manuf. 11(5), 379–394 (1997)CrossRefGoogle Scholar
  70. 70.
    Shea, K., Cagan, J., Fenves, S.J.: A shape annealing approach to optimal truss design with dynamic grouping of members. J. Mech. Des. 119(3), 388–394 (1997)CrossRefGoogle Scholar
  71. 71.
    Shim, Y.S., Kim, C.H.: Generating flying creatures using body-brain co-evolution. In: Proceedings of Symposium on Computer Animation (SCA), pp. 276–285. Eurographics Association (2003)Google Scholar
  72. 72.
    Sims, K.: Evolving 3D morphology and behavior by competition. Artif. Life 1(4), 353–372 (1994)CrossRefGoogle Scholar
  73. 73.
    Spector, L., Klein, J., Feinstein, M.: Division blocks and the open-ended evolution of development, form, and behavior. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp. 316–323. ACM (2007)Google Scholar
  74. 74.
    Stanley, K., Miikkulainen, R.: A taxonomy for artificial embryogeny. Artif. Life 9(2), 93–130 (2003)CrossRefGoogle Scholar
  75. 75.
    Stanley, K.O.: Compositional pattern producing networks: a novel abstraction of development. Genet. Program. Evol. Mach. 8(2), 131–162 (2007)MathSciNetCrossRefGoogle Scholar
  76. 76.
    Stiny, G.: Introduction to shape and shape grammars. Environ. Plan. B 7(3), 343–351 (1980)CrossRefGoogle Scholar
  77. 77.
    Su, J.: Protein unfolding behavior studied by elastic network model. Biophys. J. 94(12), 4586–4596 (2008)CrossRefGoogle Scholar
  78. 78.
    Taura, T., Nagasaka, I.: Adaptive-growth-type 3D representation for configuration design. Artif. Int. Eng. Des. Anal. Manuf. 13(3), 171–184 (1999)CrossRefGoogle Scholar
  79. 79.
    Tibert, A., Pellegrino, S.: Review of form-finding methods for tensegrity structures. Int. J. Space Struct. 18, 209–223 (2003)CrossRefGoogle Scholar
  80. 80.
    Togashi, Y., Mikhailov, A.S.: Nonlinear relaxation dynamics in elastic networks and design principles of molecular machines. Proc. Natl. Acad. Sci. U S A 104(21), 8697–8702 (2007)CrossRefGoogle Scholar
  81. 81.
    Trefzer, M.A., Kuyucu, T., Miller, J.F., Tyrrell, A.M.: A model for intrinsic artificial development featuring structural feedback and emergent growth. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 301–308. IEEE-Press (2009)Google Scholar
  82. 82.
    Vale, R.D., Milligan, R.A.: The way things move: looking under the hood of molecular motor proteins. Science 288(5463), 88–95 (2000)CrossRefGoogle Scholar
  83. 83.
    Watson, J., Geard, N., Wiles, J.: Towards more biological mutation operators in gene regulation studies. Biosystems 76(1–3), 239–248 (2004)CrossRefGoogle Scholar
  84. 84.
    Willadsen, K., Wiles, J.: Dynamics of gene expression in an artificial genome. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 185–190. IEEE-Press (2003)Google Scholar
  85. 85.
    Yang, L.W.W., Bahar, I.: Coupling between catalytic site and collective dynamics: a requirement for mechanochemical activity of enzymes. Structure 13(6), 893–904 (2005)CrossRefGoogle Scholar
  86. 86.
    Zhan, S., Miller, J.F., Tyrrell, A.M.: An evolutionary system using development and artificial genetic regulatory networks for electronic circuit design. Biosystems 98(3), 176–192 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniel Lobo
    • 1
  • Jose David Fernández
    • 2
  • Francisco J. Vico
    • 2
  1. 1.Biology Department and Tufts Center for Regenerative and Developmental BiologyTufts UniversityMedfordUSA
  2. 2.Research Group in Biomimetics (GEB), Department of Computer ScienceUniversidad de MálagaMálagaSpain

Personalised recommendations