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From artificial evolution to computational evolution: a research agenda

Nature Reviews Genetics volume 7, pages 729–735 (2006)Cite this article

Abstract

Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimization and design problems by building solutions that are 'more fit' relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve previously unimaginable or intractable computational and biological problems.

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Figure 1: The biological 'central dogma' as implemented in artificial evolution.

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References

  1. Foster, J. A. Evolutionary computation. Nature Rev. Genet. 2, 428–436 (2001).

    Article  CAS  Google Scholar 

  2. Fogel, D. (ed.) Evolutionary Computation: the Fossil Record (IEEE Press, New York, 1998).

    Book  Google Scholar 

  3. Langdon, W. B. & Gustafson, S. Genetic programming and evolvable machines: five years of reviews. Genet. Programming Evolvable Machines 6, 221–228 (2005).

    Article  Google Scholar 

  4. Cotta, C. & Merelo, J. J. The complex network of evolutionary computation authors: an initial study. Physics Abstracts [online], (2006).

    Google Scholar 

  5. Fogel, L. J., Owens, A. J. & Walsh, M. J. Artificial Intelligence through Simulated Evolution (Wiley, New York, 1966).

    Google Scholar 

  6. Holland, J. H. Adaptation in Natural and Artificial Systems 2nd edn (MIT Press, Massachusetts, 1993).

    Google Scholar 

  7. Rechenberg, I. Evolutionsstrategie 2nd edn (Frommann, Stuttgart, Germany, 1993) (in German).

    Google Scholar 

  8. Koza, J. Genetic Programming (MIT Press, Massachusetts, 1992).

    Google Scholar 

  9. Cage, P. G., Kroo, I. M. & Braun, R. D. Interplanetary trajectory optimization using a genetic algorithm. J. Astronautical Sci. 43, 59–75 (1995).

    Google Scholar 

  10. Stoica, A., Klimeck, G., Salazar-Lazaro, C., Keymeulen, D. & Thakoor, A. Evolutionary design of electronic devices and circuits. Proc. 1999 Congress Evol. Computation 1271–1278 (IEEE Press, New York, 1999).

    Google Scholar 

  11. Barnum, H., Bernstein, H. J. & Spector, L. Quantum circuits for OR and AND of ORs. J. Phys. A 33, 8047–8057 (2000).

    Article  Google Scholar 

  12. Koza, J. et al. Genetic Programming IV: Routine Human–Competitive Machine Intelligence (Kluwer Academic, Massachusetts, 2003).

    Google Scholar 

  13. Koza, J., Keane, M., Yu, J., Bennett, F. H. III & Mydlowec, W. Automatic creation of human–competative programs and controllers by means of genetic programming. Genet. Programming Evolvable Machines 1, 121–164 (2000).

    Article  Google Scholar 

  14. Vaario, J. From evolutionary computation to computational evolution. Informatica 18, 417–434 (1994).

    Google Scholar 

  15. Yeh, E. -C., Venkata, S. S. & Sumic, Z. Improved distribution system planning using computational evolution. Proc. Power Industry Comp. App. Conf. 530–536 (IEEE Press, Pittsburgh, 1995).

  16. Judson, R. Computational evolution of a model polymer that folds to a specified target conformation. Proc. Pacific Symp. Biocomputing 423–437 (World Scientific, Singapore, 1996).

  17. Morange, M. Gene function. C.R. Acad. Sci. Paris 323, 1147–1153 (2000).

    Article  CAS  Google Scholar 

  18. Ramsden, J. J. Bioinformatics: An Introduction (Kluwer Academic, Dordrecht, 2004).

    Book  Google Scholar 

  19. Kapranov, P., Cawley, S. E. & Drenkow, J. Large-scale transcriptional activity in chromosomes 21 and 22. Science 296, 916–919 (2002).

    Article  CAS  Google Scholar 

  20. Gruau, F. Genetic synthesis of modular neural networks. Proc. 5th Int. Conf. Genet. Algorithms 318–325 (Morgan Kaufmann, San Francisco, 1993).

  21. O'Neill, M. & Ryan, C. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language (Kluwer Academic, Dordrecht, 2003).

    Book  Google Scholar 

  22. Koza, J. R., Andre, D., Bennett, F. H. & Keane, M. Genetic Programming 3: Darwinian Invention and Problem Solving (Morgan Kaufman, San Francisco, 1999).

    Google Scholar 

  23. Keller, R. E. & Banzhaf, W. The evolution of genetic code in genetic programming Proc. Genet. Evol. Computation Conf. 1077–1082 (Morgan Kaufmann, San Francisco, 1999).

  24. Goldberg, D. E. The Design of Innovation: Lessons from and for Competent Genetic Algorithms (Kluwer Academic, Dordrecht, 2002).

    Book  Google Scholar 

  25. De Jong, K. Evolutionary Computation: a Unified Approach (MIT Press, Cambridge, Massachusetts, 2006).

    Google Scholar 

  26. Ramsden, J. J. Paracelsus: the measurable and the unmeasurable. Psyche Problems Persp. 4, 52–88 (2004).

    Google Scholar 

  27. Ramsden, J. J. Computational aspects of consciousness. Psyche Problems Persp. 1, 93–100 (2001).

    Google Scholar 

  28. Képès, F. Periodic transcriptional organization of the E. coli genome. J. Mol. Biol. 340, 957–964 (2004).

    Article  Google Scholar 

  29. Coen, E. The Art of Genes (Oxford Univ. Press, Oxford, 1999).

    Google Scholar 

  30. Conrad, M. in A Half-Century Survey on the Universal Turing Machine 285–307 (Oxford Univ. Press, New York, 1988).

    Google Scholar 

  31. Miller, J. F. & Downing, K. L. Evolution in materio: Looking beyond the silicon box. Proc. NASA / DoD Conf. Evolvable Hardware 167–176 (IEEE Computer Society, Virginia, 2002).

    Google Scholar 

  32. Harding, S. & Miller, J. F. Evolution in materio: A tone discriminator in liquid crystal. Proc. Congress Evol. Comp. 2004 2, 1800–1807 (2004).

    Article  Google Scholar 

  33. Dittrich, P., Ziegler, J. & Banzhaf, W. Artificial chemistries — a review. Artificial Life 7, 225–275 (2001).

    Article  CAS  Google Scholar 

  34. Sims, K. Evolving virtual creatures. Proc. 21st Annu. Conf. Comp. Graphics Interactive Technol. 15–22 (ACM Press, New York, 1994).

  35. Pollack, J. B., Lipson H., Hornby, G. & Funes, P. Three generations of automatically designed robots. Artificial Life 7, 215–223 (2001).

    Article  CAS  Google Scholar 

  36. Rieffel, J. & Pollack, J. The emergence of ontogenic scaffolding in a stochastic development environment. Lecture Notes Comp. Sci. 3102, 804–815 (Springer, Berlin, 2004).

    Article  Google Scholar 

  37. Ray, T. An approach to the synthesis of life. Proc. Artificial Life II 371–408 (Addison-Wesley, Boston, 1991).

    Google Scholar 

  38. Adami, C. An Introduction to Artificial Life (Springer, Berlin, 1999).

    Google Scholar 

  39. Lones, M. A. & Tyrrell, A. Biomimetic representation with enzyme genetic programming. Genet. Programming Evolvable Machines 3, 193–217 (2002).

    Article  Google Scholar 

  40. Érdi, P. & Barna, G. Self-organizing mechanism for the formation of ordered neural mappings. Biol. Cybern. 51, 93–101 (1984).

    Article  Google Scholar 

  41. Luthi, P. O., Preiss, A., Chopard, B. & Ramsden, J. J. A cellular automaton model for neurogenesis in drosophila. Physica D 118, 151–160 (1998).

    Article  Google Scholar 

  42. McKinney, M. Heterochrony: Beyond words. Paleobiology 25, 149–153 (1999).

    Article  Google Scholar 

  43. Ramsden, J. J. in Creatine Kinase and Brain Energy Metabolism (eds Kekelidze, T. & Holtzmann, D.) 55–58 (IOS Press, Amsterdam, 2003).

    Google Scholar 

  44. Eggenberger-Hotz, P. Genome-physics interaction as a new concept to reduce the number of genetic parameters in artificial evolution. Proc. Congress Evolutionary Computation (IEEE Press, New York, 2003).

    Google Scholar 

  45. Kuo, P. D., Leier, A. & Banzhaf, W. Evolving dynamics in an artificial regulatory network model. Lecture Notes Comp. Sci. 3242, 571–580 (Springer, Berlin, 2004).

    Article  Google Scholar 

  46. Gould, S. J. & Vrba, E. Exaptation: a missing term in the science of form. Paleobiology 8, 4–15 (1982).

    Article  Google Scholar 

  47. Berenbrink, M., Koldkjaer, P., Kepp, O. & Cossins, A. R. Evolution of oxygen secretion in fishes and the emergence of a complex physiological system. Science 307, 1752–1757 (2005).

    Article  CAS  Google Scholar 

  48. Szathmáry, E. & Maynard-Smith, J. The major evolutionary transitions. Nature 374, 227–232 (1995).

    Article  Google Scholar 

  49. Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402, C47–C52 (1999).

    Article  CAS  Google Scholar 

  50. Simon, H. A. How complex are complex systems? Proc. 1976 Biennial Meeting Philos. Sci. Assoc. 2, 507–522 (Edwards Bros, Ann Arbor, 1977).

    Google Scholar 

  51. Ashby, W. R. Requisite variety and its implications for the control of complex systems. Cybernetica 1, 84–99 (1958).

    Google Scholar 

  52. Muotri, A. R. et al. Somatic mosaicism in neuronal precursor cells mediated by L1 retrotransposition. Nature 435, 903–910 (2005).

    Article  CAS  Google Scholar 

  53. Meysenburg, M. M., Hoelting, D., McElvain, D. & Foster, J. A. How random generator quality impacts genetic algorithm performance. Proc. Genet. Evolutionary Computation Conf. 480–483 (Morgan Kaufmann, San Francisco, 2002).

  54. Altenberg, L. in Advances in Genetic Programming (ed. Kinnear, K. E.) 47–74 (MIT Press, Cambridge, Massachusetts, 1994).

    Google Scholar 

  55. Radman, M., Matic, I. & Taddei, F. Evolution of evolvability. Ann. NY Acad. Sci. 870, 146–155 (1999).

    Article  CAS  Google Scholar 

  56. Earl, D. & Deem, M. Evolvability is a selectable trait. Proc. Natl Acad. Sci. USA 101, 11531–11536 (2004).

    Article  CAS  Google Scholar 

  57. Dawkins, R. in Artificial Life (ed. Langton, C.) 201–220 (Addison Wesley, Boston, 1989).

    Google Scholar 

  58. Wagner, G. P. & Altenberg, L. Complex adaptations and the evolution of evolvability. Evolution 50, 967–976 (1996).

    Article  Google Scholar 

  59. Kirschner, M. W. & Gerhard, J. C. The Plausibility of Life (Yale Univ. Press, New Haven, 2005).

    Google Scholar 

  60. Shapiro, J. A. A 21st century view of evolution: genome system architecture, repetitive DNA, and natural genetic engineering. Gene 345, 91–100 (2005).

    Article  CAS  Google Scholar 

  61. Ramsden, J. J. & Vohradský, J. Zipf-like behavior in procaryotic protein expression. Phys. Rev. E 58, 7777–7780 (1998).

    Article  CAS  Google Scholar 

  62. Mayr, E. What is a species and what is not? Philos. Sci. 63, 262–277 (1996).

    Article  Google Scholar 

  63. Avise, J. Phylogeography: the History and Formation of Species (Harvard Univ. Press, Massachusetts, 2000).

    Google Scholar 

  64. Arber, W. in Frontiers in Biology (eds Chou, C.-H. & Shao, K.-T.) 19–24 (Academia Sinica, Taipei, 1998).

    Google Scholar 

  65. Margulis, L. & Sagan, D. Acquiring Genomes: a Theory of the Origins of Species (Basic Books, New York, 2002).

    Google Scholar 

  66. Deb, K. & Goldberg, D. An investigation of niche and species formation in genetic function optimization. Proc. 3rd Int. Conf. Genet. Algorithms 42–50 (Morgan Kaufmann, San Francisco, 1989).

  67. Fonseca, C. M. & Fleming, P. J. Multiobjective genetic algorithms made easy: selection, sharing, and mating restriction. Proc. 1st Int. Conf. Genet. Algorithms Engineering Systems Innovations Appl. 45–52 (IEEE Press, New York, 1995).

  68. International Business Machines. Autonomic computing. IBM Research | Autonomic Computing [online], (2006).

  69. Ridley, M. Evolution (Blackwell Science, Oxford, 1996).

    Google Scholar 

  70. Reil, T. Dynamics of gene expression in an artificial genome-implications for biological and artificial ontogeny. Adv. Artificial Life — Proc. 5th European Conf. Artificial Life 1674, 457–466 (1999).

    Chapter  Google Scholar 

  71. Watson, J., Geard, N. & Wiles, J. Towards more biological mutation operators in gene regulation studies. BioSystems 76, 239–248 (2004).

    Article  CAS  Google Scholar 

  72. Volkert, L. G. & M. Conrad The role of weak interactions in biological systems: the dual dynamics model. J. Theo. Bio. 193, 287–306 (1998).

    Article  CAS  Google Scholar 

  73. Bongard, J. Evolving modular genetic regulatory networks. Proc. 2002 Congress Evolutionary Computation 2, 1872–1877 (IEEE Press, New York, 2002).

    Google Scholar 

  74. Eggenberger, P. Evolving morphologies of simulated 3D organisms based on differential gene expression. Proc. 4th European Conf. Artificial Life 205–213 (MIT Press, Cambridge, Massachusetts, 1997).

    Google Scholar 

  75. Volkert, L. G. Enhancing evolvability with mutation buffering mediated through multiple weak interactions. Biosystems 69, 127–142 (2003).

    Article  Google Scholar 

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Acknowledgements

We thank the people at Genopole Recherche, Évry, France, for generously sponsoring the meeting that initiated this paper. We also thank the anonymous referees for helpful suggestions.

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Authors and Affiliations

  1. Department of Computer Science, Memorial University of Newfoundland, St John's, Newfoundland, A1B 3X5, Labrador, Canada

    Wolfgang Banzhaf

  2. Departement d'Informatique, INSA, Villeurbanne, 69621, Cedex, France

    Guillaume Beslon & Virginie Lefort

  3. School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, Ontario, Canada

    Steffen Christensen

  4. Department of Biological Sciences, University of Idaho, Moscow, 83844-3051, Idaho, USA

    James A. Foster

  5. Epigenomics Project, Genopole, 523 Terrasses de l'Agora, Evry, 91000, France

    François Képès

  6. Department of Electronics, University of York, York, YO10 5DD, UK

    Julian F. Miller

  7. Université de Paris-Descartes, Faculté de Médecine, INSERM Site Necker, U571, 156 Rue de Vaugirard, Paris, 75015, France

    Miroslav Radman

  8. Mediterranean Institute for Life Sciences, Mestrovicevo setaliste bb, Split, 2100, Croatia

    Miroslav Radman

  9. School of Applied Sciences, Cranfield University, Cranfield, MK43 0AL, Bedfordshire, UK

    Jeremy J. Ramsden

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  1. Wolfgang Banzhaf
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Correspondence to Wolfgang Banzhaf, James A. Foster or François Képès.

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Banzhaf, W., Beslon, G., Christensen, S. et al. From artificial evolution to computational evolution: a research agenda. Nat Rev Genet 7, 729–735 (2006). https://doi.org/10.1038/nrg1921

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