References:

- Abi Abdallah, D. S., Fonner, C. W., Lax, N. C., Babeji, M. R., and Palé, F. A. (2020). Evaluating the use of avida-ed digital organisms to teach evolution & natural selection. The American Biology Teacher, 82(2):114–119.
- Archer, C. T., Kim, J. F., Jeong, H., Park, J. H., Vickers, C. E., Lee, S. Y., and Nielsen, L. K. (2011). The genome sequence of e. coli w (atcc 9637): comparative genome analysis and an improved genome-scale reconstruction of e. coli. BMC Genomics, 12(1):9.
- Arrhenius, S. (1889). Über die Dissociationswärme und den Einfluss der Temperatur auf den Dissociationsgrad der Elektrolyte.
- Banzhaf, W. and Yamamoto, L. (2015). Artificial Chemistries. The MIT Press.
- Bartel, D. P. and Unrau, P. J. (1999). Constructing an rna world. Trends in Cell Biology, 9(12):M9–M13.
- Bohrer, C. H. and Xiao, J. (2020). Complex diffusion in bacteria. Adv Exp Med Biol, 1267:15–43.
- Brameier, M. F. and Banzhaf, W. (2010). Linear Genetic Programming. Springer Publishing Company, Incorporated, 1st edition.
- Bédard-Couture, R. (2024). Improved measures of robustness and evolvability for evolutionary systems. Master’s thesis, École de technologie supérieure.
- Cannon, J. R. (1984). The One-Dimensional Heat Equation. Encyclopedia of Mathematics and its Applications. Cambridge University Press.
- Ceravola, A. and Joublin, F. (2021). From json to jsen through virtual languages. Open Journal of Web Technologies (OJWT), 8(1):1–15.
- Clark, E. B., Hickinbotham, S. J., and Stepney, S. (2017). Semantic closure demonstrated by the evolution of a universal constructor architecture in an artificial chemistry. J R Soc Interface, 14(130).
- Das, B. and Mitra, P. (2021). High-performance whole-cell simulation exploiting modular cell biology principles. Journal of Chemical Information and Modeling, 61(3):1481–1492. PMID: 33683902.
- Dewdney, A. K. (1987). Computer recreations. Scientific American, 256(1):14–21.
- Douady, S. and Couder, Y. (1996). Phyllotaxis as a dynamical self organizing process part iii: The simulation of the transient regimes of ontogeny. Journal of Theoretical Biology, 178(3):295–312.
- Flamm, C., Endler, L., Müller, S., Widder, S., and Schuster, P. (2007). A minimal and self-consistent in silico cell model based on macromolecular interactions. Philos Trans R Soc Lond B Biol Sci, 362(1486):1831–1839.
- Galván-López, E., Cody-Kenny, B., Trujillo, L., and Kattan, A. (2013). Using semantics in the selection mechanism in genetic programming: A simple method for promoting semantic diversity. In 2013 IEEE Congress on Evolutionary Computation, pages 2972–2979.
- Ganti, T. (2003). The Principles of Life. Oxford Biology. OUP Oxford.
- Gillespie, D. T. (1976). A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics, 22(4):403–434.
- Gillespie, D. T. (1977). Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry, 81(25):2340–2361.
- Grogono, P. and Kharma, N. (2020). Formalizing evolvability. unpublished.
- HHMI (2025). Hhmi’s beautiful biology. https://www.hhmi.org/beautifulbiology/media-detail/b-ready
[Accessed: 29th March 2025].
- Hu, T. and Banzhaf, W. (2018). Neutrality, Robustness, and Evolvability in Genetic Programming, pages 101–117. Springer International Publishing, Cham.
- Kharma, N. and Bédard-Couture, R. (2024). Robustness and evolvability: Revisited, redefined and applied. BioSystems, 246:105281.
- Kicsiny, R., Hufnagel, L., Lóczi, L., Székely, L., and Varga, Z. (2025). Modeling the mutation and competition of certain nutrient-producing protocells by means of specific turing machines. Artificial Life, 31(1):2–30.
- Kiel, L. D. (2001). Virtual worlds: Synthetic universes, digital life and complexity edited by jean-claude heudin. J. Artificial Societies and Social Simulation, 4(3).
- Klein, J. (2002). Breve: A 3d environment for the simulation of decentralized systems and artificial life. In Proceedings of the Eighth International Conference on Artificial Life, ICAL 2003, page 329–334, Cambridge, MA, USA. MIT Press.
- Komosinski, M. and Ulatowski, S. (2009). Framsticks, pages 107–148. Springer London, London.
- Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA.
- Koza, J. R. (1994). Spontaneous emergence of self-replicating and evolutionarily self-improving computer programs. In Langton, C. G., editor, Artificial Life III, volume XVII of SFI Studies in the Sciences of Complexity, pages 225–262. Addison-Wesley, Santa Fe, New Mexico, USA.
- Krawiec, K. (2016). Behavioral Program Synthesis with Genetic Programming, volume 618 of Studies in Computational Intelligence. Springer International Publishing. http://www.cs.put.poznan.pl/kkrawiec/bps.
- Krawiec, K. and Lichocki, P. (2009). Approximating geometric crossover in semantic space. In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO ’09, pages 987–994, New York, NY, USA. ACM.
- Krawiec, K. and Lichocki, P. (2010). Using Co-solvability to Model and Exploit Synergetic Effects in Evolution, pages 492–501. Springer Berlin Heidelberg, Berlin, Heidelberg.
- Krawiec, K. and Pawlak, T. (2013). Locally geometric semantic crossover: a study on the roles of semantics and homology in recombination operators. Genetic Programming and Evolvable Machines, 14(1):31–63.
- Levenshtein, V. I. (1966). Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady, 10:707.
- Macía, J. and Solé, R. V. (2007a). Protocell self-reproduction in a spatially extended metabolism–vesicle system. Journal of Theoretical Biology, 245(3):400–410.
- Macía, J. and Solé, R. V. (2007b). Synthetic turing protocells: vesicle self-reproduction through symmetry-breaking instabilities. Philos Trans R Soc Lond B Biol Sci, 362(1486):1821–1829.
- Mano, M. (1979). Digital Logic & Computer Design. Pearson Education.
- McKay, R. I. (2000a). Committee learning of partial functions in fitness-shared genetic programming. In 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies, volume 4, pages 2861–2866 vol.4. McKay, R. I. B. (2000b). Fitness sharing in genetic programming. In Proceedings of the 2Nd Annual Conference on Genetic and Evolutionary Computation, GECCO’00, pages 435–442, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
- Miller, J., editor (2011). Cartesian Genetic Programming. Springer Berlin Heidelberg, Berlin, Heidelberg.
- Miller, J. F. and Thomson, P. (2000). Cartesian Genetic Programming, pages 121–132. Springer Berlin Heidelberg, Berlin, Heidelberg.
- Miller, J. F., Thomson, P., and Fogarty, T. (1997). Designing electronic circuits using evolutionary algorithms. arithmetic circuits: A case study.
- Mogensen, T. (2022). Programming Language Design and Implementation. Texts in Computer Science. Springer International Publishing.
- Murray, J. D. (c1989.). Mathematical biology /. Springer-Verlag„ Berlin ;. Includes index.
- Ofria, C., Bryson, D., and Wilke, C. (2009a). Avida: A Software Platform for Research in Computational Evolutionary Biology, volume 10, pages 3–36.
- Ofria, C., Bryson, D. M., and Wilke, C. O. (2009b). Avida, pages 3–35. Springer London, London.
- Oltean, M. and Grosan, C. (2003). A comparison of several linear genetic programming. TECHNIQUES, COMPLEXSYSTEMS, 14:282–311.
- Packard, N., Bedau, M. A., Channon, A., Ikegami, T., Rasmussen, S., Stanley, K., and Taylor, T. (2019). Open-Ended Evolution and Open-Endedness: Editorial Introduction to the Open-Ended Evolution I Special Issue. Artificial Life, 25(1):1–3.
- Pargellis, A. (2003). Self-organizing genetic codes and the emergence of digital life. Complexity, 8:69–78.
- Pattis, R. E. (2025). Ebnf: A notation to describe syntax. [Online; accessed 21-Jan-2025].
- Petke, J., Harman, M., Langdon, W. B., and Weimer, W. (2014). Using Genetic Improvement and Code Transplants to Specialise a C++ Program to a Problem Class, pages 137–149. Springer Berlin Heidelberg, Berlin, Heidelberg.
- Pohorille, A., Chipot, C., New, M. H., and Wilson, M. A. (1996). Molecular modeling of protocellular functions. Pac Symp Biocomput, pages 550–569.
- Rashevsky, N. (1958). Mathematical Biophysics: Physicomathematical Foundations of Biology. University of Chicago Press.
- Rasmussen, S., Knudsen, C., Feldberg, P., and Hindsholm, M. (1990a). The coreworld: Emergence and evolution of cooperative structures in a computational chemistry. In Proceedings of the Ninth Annual International Conference of the Center for Nonlinear Studies on Self-Organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent Computation, CNLS ’89, page 111–134, NLD. North-Holland Publishing Co.
- Rasmussen, S., Knudsen, C., Feldberg, P., and Hindsholm, M. (1990b). The coreworld: emergence and evolution of cooperative structures in a computational chemistry. Physica D: Nonlinear Phenomena, 42:111–134.
- Ray, T. S. (1991). An approach to the synthesis of life. In C. Langton, C. Taylor, J. D. Farmer, S. Rasmussen, editor, Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity, volume vol. XI, pages 371–408. Addison-Wesley, Redwood City, CA.
- Ray, T. S. (2003). An Evolutionary Approach to Synthetic Biology: Zen in the Art of Creating Life, pages 479–517. Springer Berlin Heidelberg, Berlin, Heidelberg.
- Richard, P., Pitard, B., and Rigaud, J.-L. (1995). Atp synthesis by the f0f1-atpase from the thermophilic bacillus ps3 co-reconstituted with bacteriorhodopsin into liposomes: Evidence for stimulation of atp synthesis by atp bound to a noncatalytic binding site. Journal of Biological Chemistry, 270(37):21571–21578.
- Sauro, H. (2013). Enzyme Kinetics for Systems Biology. Ambrosius Publishing.
- Sienkiewicz, R. (2010). The particle methods for simulation of emergent phenomena. PhD thesis, Gda´nsk University of Technology, Faculty of Electronics, Telecommunication and Informatics, Gda´nsk, Poland.
- Sienkiewicz, R. and J˛edruch, W. (2011). The universal constructor in the digihive environment. In Kampis, G., Karsai, I., and Szathmáry, E., editors, Advances in Artificial Life. Darwin Meets von Neumann, pages 183–190, Berlin, Heidelberg. Springer Berlin Heidelberg.
- Smith, A. (2020). Universality of wolfram’s 2, 3 turing machine. Complex Systems, 29.
- Smith, R. E., Forrest, S., and Perelson, A. S. (1993). Searching for diverse, cooperative populations with genetic algorithms. Evolutionary computation, 1(2):127–149.
Solé, R. V., Munteanu, A., Rodriguez-Caso, C., and Macía, J. (2007). Synthetic protocell biology: from reproduction to computation. Philos. Trans. R. Soc. Lond. B Biol. Sci., 362(1486):1727–1739.
- Solé, R. V., Munteanu, A., Rodriguez-Caso, C., and Macía, J. (2007). Synthetic protocell biology: from reproduction to computation. Philos Trans R Soc Lond B Biol Sci, 362(1486):1727–1739.
- Spector, L. and Robinson, A. (2002). Genetic programming and autoconstructive evolution with the push programming language. Genetic Programming and Evolvable Machines, 3(1):7–40.
- Szenk, M., Dill, K. A., and de Graff, A. M. (2017). Why do fast-growing bacteria enter overflow metabolism? testing the membrane real estate hypothesis. Cell Systems, 5(2):95–104.
- Szostak, J. W., Bartel, D. P., and Luisi, P. L. (2001). Synthesizing life. Nature, 409(6818):387–390.
- Taylor, T. and Hallam, J. (1997). Studying evolution with self-replicating computer programs.
- Taylor, T. J. (1999). From Artificial Evolution to Artificial Life. PhD thesis, University of Edinburgh.
- Turing, A. M. (1937). On Computable Numbers, with an Application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, s2-42(1):230–265.
- Uy, N. Q., Hoai, N. X., O’Neill, M., McKay, R. I., and Galván-López, E. (2011). Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genetic Programming and Evolvable Machines, 12(2):91–119.
- Uy, N. Q., Hoai, N. X., O’Neill, M., McKay, R., and Phong, D. N. (2013). On the roles of semantic locality of crossover in genetic programming. Information Sciences, 235:195 – 213. Data-based Control, Decision, Scheduling and Fault Diagnostics.
- Varetto, L. (1993). Typogenetics: an artificial genetic system. Journal of theoretical biology, 160(2):185—205.
- Wagner, A. (2008). Robustness and evolvability: a paradox resolved. Proceedings of the Royal Society B: Biological Sciences, 275:100 – 91.
- Wilson, G. and Banzhaf, W. (2008). A comparison of cartesian genetic programming and linear genetic programming. In Proceedings of the 11th European Conference on Genetic Programming, EuroGP’08, pages 182–193, Berlin, Heidelberg. Springer-Verlag.
- Wolfram, S. (2002). A New Kind of Science. Wolfram Media.
- Zachar, I., Fedor, A., and Szathmáry, E. (2011). Two different template replicators coexisting in the same protocell: stochastic simulation of an extended chemoton model. PLoS One, 6(7):e21380.