Abstract
Artificial multi-cellular organisms develop from a single zygote to different structures and shapes, some simple, some complex. Such phenotypic structural complexity is the result of morphogenesis, where cells grow and differentiate according to the information encoded in the genome. In this paper we investigate the structural complexity of artificial cellular organisms at phenotypic level, in order to understand if genome information could be used to predict the emergent structural complexity. Our measure of structural complexity is based on the theory of Kolmogorov complexity and approximations. We relate the Lambda parameter, with its ability to detect different behavioral regimes, to the calculated structural complexity. It is shown that the easily computable Lempel-Ziv complexity approximation has a good ability to discriminate emergent structural complexity, thus providing a measurement that can be related to a genome parameter for estimation of the developed organism’s phenotypic complexity. The experimental model used herein is based on 1D, 2D and 3D Cellular Automata.
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References
Doursat, R., Sánchez, C., Dordea, R., Fourquet, D., Kowaliw, T.: Embryomorphic engineering: emergent innovation through evolutionary development. In: Doursat, R., Sayama, H., Michel, O. (eds.) Morphogenetic Engineering. UCS Series, pp. 275–311. Springer (2012)
Kumar, S., Bentley, P.J.: Biologically inspired evolutionary development. In: Tyrrell, A.M., Haddow, P.C., Torresen, J. (eds.) ICES 2003. LNCS, vol. 2606, pp. 57–68. Springer, Heidelberg (2003)
Miller, J.F., Banzhaf, W.: Evolving the program for a cell: from French flag to boolean circuits. In: Kumar, S., Bentley, P.J. (eds.) On Growth, Form and Computers, pp. 278–301. Elsevier Limited, Oxford (2003)
Tufte, G.: Evolution, development and environment toward adaptation through phenotypic plasticity and exploitation of external information. In: Bullock, S., Noble, J., Watson, R., Bedau, M.A. (eds.) Artificial Life XI, pp. 624–631. MIT Press, Cambridge (2008)
Li, M., Vitanyi, P.M.B.: An introduction to Kolmogorov complexity and its applications, 2nd edn. Springer, New York (1997)
Langton, C.G.: Computation at the edge of chaos: phase transitions and emergent computation. In: Forrest, S. (ed.) Emergent Computation, pp. 12–37. MIT Press (1991)
Wolfram, S.: Universality and complexity in CA. Physica D 10(1-2), 1–35 (1984)
Kolmogorov, A.N.: Three approaches to the quantitative definition of information. Problems Inform. Transmission 1(1), 1–7 (1965)
Turing, A.: On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, Series 2 42, 230–265 (1936)
Hartmann, M., Lehre, P.K., Haddow, P.C.: Evolved digital circuits and genome complexity. In: NASA/DoD Conference on Evolvable Hardware 2005, pp. 79–86. IEEE Press (2005)
Lehre, P.K., Haddow, P.C.: Developmental mappings and phenotypic complexity. In: Proceedings of 2003 IEEE CEC, vol. 1, pp. 62–68. IEEE Press (2003)
Deutsch, L.P.: DEFLATE Compressed data format specification version 1.3 (1996)
Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Transactions on Information Theory 23(3), 337–343 (1977)
Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proceedings of the I.R.E., 1098–1102 (1952)
Tufte, G., Nichele, S.: On the correlations between developmental diversity and genomic composition. In: 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, pp. 1507–1514. ACM (2011)
Nichele, S., Tufte, G.: Genome parameters as information to forecast emergent developmental behaviors. In: Durand-Lose, J., Jonoska, N. (eds.) UCNC 2012. LNCS, vol. 7445, pp. 186–197. Springer, Heidelberg (2012)
Kowaliw, T.: Measures of complexity for artificial embryogeny. In: GECCO 2008, pp. 843–850. ACM (2008)
Ulam, S.: Los Alamos National Lab 1909-1984, vol. 15(special issue), pp. 1–318. Los Alamos Science, Los Alamos (1987)
von Neumann, J.: Theory and organization of complicated automata. In: Burks, A.W. (ed.), pp. 29–87 (2, part one). Based on transcript of lectures delivered at the University of Illinois (December 1949)
Zenil, H., Villareal-Zapata, E.: Asymptotic behaviour and ratios of complexity in cellular automata. arXiv/1304.2816 (2013)
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Nichele, S., Tufte, G. (2014). Measuring Phenotypic Structural Complexity of Artificial Cellular Organisms. In: Abraham, A., Krömer, P., Snášel, V. (eds) Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-319-01781-5_3
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DOI: https://doi.org/10.1007/978-3-319-01781-5_3
Publisher Name: Springer, Cham
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