Skip to main content

Bacterially inspired evolution of intelligent systems under constantly changing environments

Abstract

This paper explores the capabilities of open-ended bio-inspired evolutionary construction of intelligent systems under changing environments. We present and analyze extensive results of the bacterial evolutionary system. This system creates 3D environments that simulate real constantly changing environments. Populations of artificial bacteria constantly evolve their inner biological processes in these environments as they perform every action programmed in their life cycle. This results in a decentralized, asynchronous, parallel and self-adapting general-purpose evolutionary process whose only goal is the survival of the bacterial population under successive, continuously changing environmental conditions. Results show the problem independence and general-purpose capabilities of the system by making it evolve fuzzy rule-based systems under different environments. Robustness and fault tolerance capabilities are also tested by subjecting the bacterial evolutionary system to sudden changes in the environment. Evolution is open-ended as there is no need to restart the system when changes take place. Artificial bacteria self-adapt themselves in real time in order to guarantee their survival.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

References

  1. Abelson H, Allen D, Coore D, Hanson C, Rauch E, Sussman GJ, Weiss R (2000) Amorphous computing. Commun ACM 43(5):74–82

  2. Barrios Rolania D, Font JM, Manrique D (2012) Bacterially inspired evolving system with an application to time series prediction. Appl Soft Comput 13(2):1136–1146

  3. Bassler BL, Losick R (2006) Bacterially speaking. Cell 125:237–246

    Article  Google Scholar 

  4. Bonabeau E, Corne D, Poli R (2010) Swarm intelligence: the state of the art special issue of natural computing. Nat Comput 9:655–657

    Article  MathSciNet  Google Scholar 

  5. Couchet J, Font JM, Manrique D (2008) Using evolved fuzzy neural networks for injury detection from isokinetic curves. In: Applications and innovations in intelligent systems XVI. Proceedings of the AI2008, vol II. Cambridge, pp 223–238

  6. De Jong K (2009) Evolutionary computation. Wiley Interdiscip Rev: Comput Stat 1(1):52–56

    Article  Google Scholar 

  7. DemattT L, Priami C, Romanel A, Soyer O (2008) Evolving blenx programs to simulate the evolution of biological networks. Theor Comput Sci 408:83–96

    Article  Google Scholar 

  8. Eiben A, Ferreira N, Schut M, Kernbach S (2011) Evolution of things. arXiv:1106.0190

  9. Font JM, Manrique D (2010) Grammar-guided evolutionary automatic system for autonomously building biological oscillators. WCCI 2010. IEEE World Congress on Computational Intelligence. Barcelona, pp 2742–2748

  10. Font JM, Manrique D, Rios J (2010) Evolutionary construction and adaptation of intelligent systems. Expert Syst Appl 37:7711–7720

  11. Frantois P, Hakim V (2004) Design of genetic networks with specified functions by evolution in silico. PNAS 101(2):580–585

    Article  Google Scholar 

  12. Frantois P, Hakim V, Seggia ED (2007) Deriving structure from evolution: metazoan segmentation. Mol Syst Biol 3:154

  13. Garcia-Ojalvo J, Elowitz MB, Strogatz SH (2004) Modeling a synthetic multicellular clock: repressilators coupled by quorum sensing. PNAS 101(30):10955–10960

    Article  MATH  MathSciNet  Google Scholar 

  14. Garzon M, Blain D, Bobba K, Neel A, West M (2003) Self-assembly of dna-like structures in silico. Gen Progr Evol Mach 4:185–200

    Article  Google Scholar 

  15. Groß R, Magnenat S, Küchler L, Massaras V, Bonani M, Mondada F (2011) Towards an autonomous evolution of non-biological physical organisms. In: Kampis G, Karsai I, Szathmáry E (eds) Advances in artificial life. Darwin Meets von Neumann. Lecture Notes in Computer Science, vol 5778. Springer, Heidelberg, pp 173–180

  16. Harvey I (2011) The microbial genetic algorithm. In: Kampis G, Karsai I, Szathmáry E (eds) Advances in Artificial Life. Darwin Meets von Neumann. Lecture Notes in Computer Science, vol 5778. Springer, Heidelberg, pp 126–133

  17. Hintze A, Adami C (2008) Evolution of complex modular biological networks. PloS Comput Biol 4(2):e23

    Article  MathSciNet  Google Scholar 

  18. Huijsman R, Haasdijk E, Eiben A (2011) An on-line, on-board distributed algorithm for evolutionary robotics. In: Proceedings of artificial evolution, 10th international conference on evolution artificielle, EA

  19. Ikebukuro K, Yoshida W, Noma T, Sode K (2006) Analysis of the evolution of the thrombin-inhibiting dna aptamers using a genetic algorithm. Biotechnol Lett 28:1933–1937

    Article  Google Scholar 

  20. Kari L, Rozenberg G (2008) The many facets of natural computing. Commun ACM 51(10):72–83

    Article  Google Scholar 

  21. Kawamata I, Tanaka F, Hagiya M (2009) Automatic design of dna logic gates based on kinetic simulation. In: DNA 15, vol 5877 of LNCS. pp 88–96

  22. Khalil AS, Collins JJ (2010) Synthetic biology: applications come of age. Nat Rev Genet 11:367–379

    Article  Google Scholar 

  23. Kiehl TR (2009) Evolving biochemical reaction networks with stochastic attributes. In: Proceedings of the 11th annual conference companion on genetic and evolutionary computation conference: late breaking papers. GECCO ’09. ACM, New York, pp 2065–2070

  24. Klir G, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall PTR, New Jersey

    MATH  Google Scholar 

  25. Macdonald J, Stefanovic D, Stojanovic MN (2008) Dna computers for work and play. Sci Am 299:84–91

    Article  Google Scholar 

  26. Marini F (2009) Artificial neural networks in foodstuff analyses: trends and perspectives. A review. Anal Chim Acta 635:121–131

    Article  Google Scholar 

  27. Mattiussi C, Marbach D, Dnrr P, Floreano D (2008) The age of analog networks. AI Mag 29(3):63–76

    Google Scholar 

  28. Paladugu SR, Chickarmane V, Deckard A, Frumkin JP, McCormack M, Sauro HM (2006) In silico evolution of functional modules in biochemical networks. IEE Proc Syst Biol 153(4):223–235

    Article  Google Scholar 

  29. Qian L, Winfree E (2009) A simple dna motif for synthesizing large-scale circuits. In: DNA 14, vol 5347 of LNCS. pp 70–89

  30. Reshes G, Vanounou S, Fishov I, Feingold M (2008) Cell shape dynamics in escherichia coli. Biophys J 94:251–264

    Article  Google Scholar 

  31. Rodrigo G, Carrera J, Jaramillo A (2007) Genetdes: automatic design of transcriptional networks. Bioinformatics 23(14):1857–1858

    Article  Google Scholar 

  32. Rodrigo G, Carrera J, Jaramillo A (2008) Computational design and evolution of the oscillatory response under light-dark cycles. Biochimie 90:888–897

  33. Rouilly V, Canton B, Nielsen P, Kitney R (2007) Registry of biobricks models using cellml. BMC Syst Biol 1(Suppl 1):79–80

  34. Segall JE, Block SM, Berg HC (1986) Temporal comparisons in bacterial chemotaxis. PNAS 83(23):8987–8991

    Article  Google Scholar 

  35. Shin S, Lee I, Kim D, Zhang B (2005) Multiobjective optimization of dna sequences for reliable dna computing. IEEE Trans Evol Comput 9(2):143–158

    Article  Google Scholar 

  36. Stewart EJ, Madden R, Paul G, Taddei F (2005) Aging and death in an organism that reproduces by morphologically symmetric division. PLoS Biol 3(2):295–300

    Article  Google Scholar 

  37. Tagkopoulos I, Liu Y, Tavazoie S (2008) Predictive behavior within microbial genetic networks. Science 30:1313–1317

    Article  Google Scholar 

  38. Timmis J, Amos M, Banzhaf W, Tyrrell A (2006) “Going back to our roots”: second generation biocomputing. J Unconv Comput 2:349–378

    Google Scholar 

  39. Trueba P, Prieto A, Caama no P, Bellas F, Duro R (2011) Task-driven species in evolutionary robotic teams. In: Ferrández JM, Álvarez Sánchez JR, de la Paz F, Toledo FJ (eds) Foundations on Natural and Artificial Computation. Lecture Notes in Computer Science. vol 6686, Springer, Heidelberg, pp 138–147

  40. Watson RA, Ficici SG, Pollack JB (2002) Embodied evolution: distributing an evolutionary algorithm in a population of robots. Robots Auton Syst 39:1–18

    Article  Google Scholar 

  41. Wiese K, Deschenes A, Hendriks A (2008) Rnapredict—an evolutionary algorithm for RNA secondary structure prediction. IEEE/ACM Trans Comput Biol Bioinform 5(1):25–41

    Article  Google Scholar 

  42. Wischmann S, Stamm K, Wörgötter F (2007) Embodied evolution and learning: the neglected timing of maturation. In: ECAL 2007, vol 4648 of LNAI. pp 284–293

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to J. M. Font.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Barrios Rolanía, D., Font, J.M. & Manrique, D. Bacterially inspired evolution of intelligent systems under constantly changing environments. Soft Comput 19, 1071–1083 (2015). https://doi.org/10.1007/s00500-014-1319-4

Download citation

Keywords

  • Natural computing
  • Bio-inspired computation
  • Evolutionary computation
  • Open-ended evolution