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Impact of ALife Simulation of Darwinian and Lamarckian Evolutionary Theories

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Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 8))

Abstract

Until nowadays, the scientific community firmly rejected the Theory of Inheritance of Acquired Characteristics, a theory mostly associated with the name of Jean-Baptiste Lamarck (1774–1829). Though largely dismissed when applied to biological organisms, this theory found its place in a young discipline called Artificial Life. Based on the two models of Darwinian and Lamarckian evolutionary theories built using neural networks and genetic algorithms, this research presents a notion of the potential impact of implementation of Lamarckian knowledge inheritance across disciplines, including biology, computer science and philosophy. There is an evidence that Lamarckian organisms can have wide practical application across several different domains, therefore this type of research should be allowed and encouraged. However, even though Lamarckian evolutionary algorithm already holds major benefits for various disciplines and promises even more, its implementation in Artificial Life needs regulation to avoid malevolent use.

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References

  1. Sasaki, T. and Tokoro, M.: Comparison between Lamarckian and Darwinian Evolution on a Model Using Neural Networks and GAs. Knowledge and Information Systems (KAIS): An International Journal, Springer. (2000).

    Google Scholar 

  2. Ross, B. J.: A Lamarckian Evolution Strategy for Genetic Algorithms. In Lance D. Chambers, editor, Practical Handbook of Genetic Algorithms: Complex Coding Systems, Boca Raton, FL: CRC Press, volume III, pp. 1–16. (1999).

    Google Scholar 

  3. Neumann, J. v.: The General and Logical Theory of Automata. Collected Works Volume V: Design of Computers, Theory of Automata and Numerical Analysis. Ed. A.H. Taub. New York: Pergamon, p. 288–326. (1963).

    Google Scholar 

  4. Rumelhart, D. and McClelland, J.: Parallel Distributed Processing. MIT Press, Cambridge, Mass. (1986).

    Google Scholar 

  5. Holland, J. H.: Adaptation in Natural and Artificial Systems. Ann Arbor MI University of Michigan Press (Vol. Ann Arbor). (1975).

    Google Scholar 

  6. Bull, J. J. and Wichman, H. A.: Applied Evolution. Annual Review of Ecology, Evolution, and Systematics, 32, 183–217. (2001).

    Google Scholar 

  7. Sandberg, A. and Bostrom, N.: Whole Brain Emulation: A Roadmap Technical Report. Future of Humanity Institute, Oxford University. (2008).

    Google Scholar 

  8. Sandberg, A.: Ethics of brain emulations. Journal of Experimental & Theoretical Artificial Intelligence, (May), 1–19. (2014).

    Google Scholar 

  9. Lewis, P.R., Platzner, M., Rinner, B., Torresen, J., and Yao, X.: Self-aware Computing Systems. An Engineering Approach. Springer, Natural Computing Series. (2016).

    Google Scholar 

  10. Wang, B.: Ray Kurzweil Clarifies his Vision of Reverse Engineering the Brain and Developing Artificial Intelligence from Principles Gleaned from Brain Science, Next Big Future: Coverage of Disruptive Science and Technology (2010). http://www.nextbigfuture.com/2010/08/ray-kurzweil-clarifies-his-vision-of.html?m=1.

  11. Djurfeldt, M., Lundqvist, M., Johansson, C., Rehn, M., Ekeberg, O., and Lansner, A.: Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer. IBM Journal of Research and Development, 52, 31–41. (2008).

    Google Scholar 

  12. Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., and Rasmussen, D.: A large-scale model of the functioning brain. Science, 338, 1202–1205. (2012).

    Google Scholar 

  13. Markram, H.: The blue brain project. Nature Reviews Neuroscience, 7, 153–160. (2006).

    Google Scholar 

  14. Preissl, R., Wong, T. M., Datta, P., Flickner, M. D., Singh, R., Esser, S. K., Modha, D. S.: Compass: A scalable simulator for an architecture for cognitive computing. Proceedings of Supercomputing 2012, Salt Lake City, November 10 –16, 2012. (2012).

    Google Scholar 

  15. Fraser, A. S.: Monte Carlo analyses of genetic models. Nature 181 (4603): 9–208. (1958).

    Google Scholar 

  16. Rechenberg, I.: Evolutionsstrategie – Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis) (in German). Fromman-Holzboog. (1973).

    Google Scholar 

  17. Koza, J. R.: Genetic Programming. MIT Press. (1992).

    Google Scholar 

  18. Jamshidi, M.: Tools for intelligent control: fuzzy controllers, neural networks and genetic algorithms. Philosophical Transactions of the Royal Society A 361 (1809): 1781–808. (2003).

    Google Scholar 

  19. Huerta, M., Haseltine, F., Liu, Y., Downing, G., and Seto, B.: NIH Working Definition of Bioinformatics and Computational Biology. BISTIC Definition Committee. (2000).

    Google Scholar 

  20. Kumar, D. and Bhatnagar, R.: An approach implements artificial intelligence into human life with new technologies and application. International Journal on Emerging Technologies 1(2). (2010).

    Google Scholar 

  21. Hinton, G. E. and Nowlan, S. J.: How Learning Can Guide Evolution. Complex Systems, 1, 495–502. (1987).

    Google Scholar 

  22. Bedau, M. A.: Leonardo. The Scientific and Philosophical Scope of Artificial Life. MIT Press Vol. 35, No. 4, p. 395–400. (2002).

    Google Scholar 

  23. Grefenstette, J. J., Ramsey, C. L., and Schultz, A. C.: Learning sequential decision rules using simulation models and competition. Machine Learning, 5(4), 355–381. (1990).

    Google Scholar 

  24. Davidor, Y.: Genetic Algorithms and Robotics. World Scientific Series in Robotics and Intelligent Systems: Volume 1. (Weizmann Inst. Sci., Israel). (1991).

    Google Scholar 

  25. Bray, D.: Wetware: A Computer in Every Living Cell. Yale University Press: New Haven, Connecticut. (2011).

    Google Scholar 

  26. Borresen, J. and Lynch, S.: Neuronal computers. Nonlinear Analysis, Theory, Methods and Applications, 71(12). (2009).

    Google Scholar 

  27. Pilpel, Y.: Realizing Lamarckian Evolution. Center for Bits and Atoms. MIT Media Lab, E14–633. (2016).

    Google Scholar 

  28. Moritz, C.: Biology 1B—Evolution Lecture 1. Introduction to Evolution. UC Berkeley. (2010).

    Google Scholar 

  29. Stewart, R. C.: The Journal of Evolutionary Philosophy. The Academy of Evolutionary Metaphysics. (2005).

    Google Scholar 

  30. Gibson, D. G., Glass, J. I., Lartigue, C., Noskov, V. N., Chuang, R.-Y., Algire, M. a, … Venter, J. C.: Creation of a bacterial cell controlled by a chemically synthesized genome. Science (New York, N.Y.), 329(5987), 52–56. (2010).

    Google Scholar 

  31. Morris, G. M., Goodsell, D. S., Halliday, R. S., Huey, R., Hart, W. E., Belew, R. K., and Olson, A. J.: Automated Docking Using a Lamarckian Genetic Algorithm and an Empirical Binding Free Energy Function. Journal of Computational Chemistry, 19, 1639–1662. (1998).

    Google Scholar 

  32. Collins, R. J. and Jefferson, D.: Antfarm: Towards Simulated Evolution. In Artificial Life II (pp. 579–601). Retrieved from http://citeseer.ist.psu.edu/collins91antfarm.html. (1992).

  33. Fausett, L.: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall. (1994).

    Google Scholar 

  34. Dolhansky, B.: Artificial Neural Networks: Matrix Form (Part 5). ML Primers, Neural Networks. Retrieved from http://briandolhansky.com/blog/2014/10/30/artificial-neural-networks-matrix-form-part-5. (2014).

  35. Domeniconi, C.: Proposal of a Darwin-Neural Network for a Robot Implementation. Perspectives in Neural Computing, Taylor Ed., p. 186–193. (1996).

    Google Scholar 

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Correspondence to Yuliya Betkher .

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Betkher, Y., Nabais, N., Santos, V. (2017). Impact of ALife Simulation of Darwinian and Lamarckian Evolutionary Theories. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-49049-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-49049-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49048-9

  • Online ISBN: 978-3-319-49049-6

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