Below is the full bibliography of references used in this Encyclopedia. For a searchable version of this bibliography or to export the references in a format of your choice, see the ISAL Zotero Library

Adami, C. (1998). Introduction to artificial life. Springer.
Adami, C. (2021). A Brief History of Artificial Intelligence Research. Artificial Life, 27(2), 131–137. https://doi.org/10/gncf9t
Adami, C., Ofria, C., & Collier, T. C. (2000). Evolution of biological complexity. Proceedings of the National Academy of Sciences, 97(9), 4463–4468. https://doi.org/10/drdhb5
Armstrong, R. (2015). How do the origins of life sciences influence 21st century design thinking? 2–11. https://doi.org/10.1162/978-0-262-33027-5-ch002
Baile, E. M., Dahlby, R. W., Wiggs, B. R., & Pare, P. D. (1985). Role of tracheal and bronchial circulation in respiratory heat exchange. Journal of Applied Physiology, 58(1), 217–222. https://doi.org/10.1152/jappl.1985.58.1.217
Baldock, C., Oberhauser, A. F., Ma, L., Lammie, D., Siegler, V., Mithieux, S. M., Tu, Y., Chow, J. Y. H., Suleman, F., Malfois, M., Rogers, S., Guo, L., Irving, T. C., Wess, T. J., & Weiss, A. S. (2011). Shape of tropoelastin, the highly extensible protein that controls human tissue elasticity. Proceedings of the National Academy of Sciences, 108(11), 4322–4327. https://doi.org/10.1073/pnas.1014280108
Batut, B., Parsons, D. P., Fischer, S., Beslon, G., & Knibbe, C. (2013). In silico experimental evolution: a tool to test evolutionary scenarios. BMC Bioinformatics, 14(15), S11. https://doi.org/10/gb8v6q
Beckmann, B. E., McKinley, P. K., Knoester, D. B., & Ofria, C. (2007). Evolution of Cooperative Information Gathering in Self-Replicating Digital Organisms. First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007), 65–76. https://doi.org/10/ddn64f
Beckmann, B. E., McKinley, P. K., & Ofria, C. (2007). Evolution of an Adaptive Sleep Response in Digital Organisms. In F. Almeida e Costa, L. M. Rocha, E. Costa, I. Harvey, & A. Coutinho (Eds.), Advances in Artificial Life (pp. 233–242). Springer. https://doi.org/10/djnpz6
Bedau, M. A., Snyder, E., & Packard, N. H. (1998). A classification of long-term evolutionary dynamics. In C. Adami, R. K. Belew, H. Kitano, & C. E. Taylor (Eds.), Artificial Life VI: Proceedings of the Sixth International Conference on Artificial Life (pp. 228–237). MIT Press.
Bedau, M. A., Snyder, E., Brown, C. T., & Packard, N. H. (1997). A comparison of evolutionary activity in artificial evolving systems and in the biosphere. In P. Husbands & I. Harvey (Eds.), Proceedings of the Fourth European Conference on Artificial Life, ECAL97 (pp. 125–134). MIT Press.
Benner, S. A., & Sismour, A. M. (2005). Synthetic biology. Nature Reviews Genetics, 6(7), 533–543. https://doi.org/10.1038/nrg1637
Bhagwat, V. M., & Ramachandran, B. V. (1975). Malathion A and B esterases of mouse liver-I. Biochemical Pharmacology, 24(18), 1713–1717. https://doi.org/10.1016/0006-2952(75)90011-8
Bhagwat, V. M., & Ramachandran, B. V. (1975). Malathion A and B esterases of mouse liver-I. Biochemical Pharmacology, 24(18), 1713–1717. https://doi.org/10.1016/0006-2952(75)90011-8
Birhane, A. (2021). The Impossibility of Automating Ambiguity. Artificial Life, 27(1), 44–61. https://doi.org/10/gj8qww
Biswas, R., Bryson, D., Ofria, C., & Wagner, A. (2014). Causes vs Benefits in the Evolution of Prey Grouping. 641–648. https://doi.org/10.1162/978-0-262-32621-6-ch103
Boden, M. A. (Ed.). (1996). The philosophy of artificial life. Oxford University Press.
Bohm, C., G., N. C., & Hintze, A. (2017). MABE (Modular Agent Based Evolver): A framework for digital evolution research. Artificial Life Conference Proceedings, 76–83. https://doi.org/10.1162/isal_a_016
Bongard, J. C. (2013). Evolutionary robotics. Communications of the ACM, 56(8), 74–83. https://doi.org/10/gkdf3m
Bongard, J. (2010). The Utility of Evolving Simulated Robot Morphology Increases with Task Complexity for Object Manipulation. Artificial Life, 16(3), 201–223. https://doi.org/10/fnx622
Borg, J. M., & Channon, A. (2021). The Effect of Social Information Use Without Learning on the Evolution of Social Behavior. Artificial Life, 26(4), 431–454. https://doi.org/10/gg98
Brant, J. C., & Stanley, K. O. (2017). Minimal criterion coevolution: a new approach to open-ended search. 67–74. https://doi.org/10/ggcgzb
Bryson, D. M., & Ofria, C. (2013). Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures. PLOS ONE, 8(12), e83242. https://doi.org/10/gkngpg
Bryson, D. M., Wagner, A. P., & Ofria, C. (2014). There and back again: gene-processing hardware for the evolution and robotic deployment of robust navigation strategies. 689–696. https://doi.org/10/gncq6j
Buehler, M. J., & Wong, S. Y. (2007). Entropic Elasticity Controls Nanomechanics of Single Tropocollagen Molecules. Biophysical Journal, 93(1), 37–43. https://doi.org/10.1529/biophysj.106.102616
Bull, L. (2021). On the Emergence of Intersexual Selection: Arbitrary Trait Preference Improves Female-Male Coevolution. Artificial Life, 27(1), 15–25. https://doi.org/10/gnc3qw
Bull, L. (2021). Are Artificial Dendrites Useful in Neuro-Evolution? Artificial Life, 27(2), 75–79. https://doi.org/10/gnc3q2
Cameron, A., Dorchen, S., Doore, S., & Vostinar, A. E. (2022). Keep Your Frenemies Closer: Bacteriophage That Benefit Their Hosts Evolve to be More Temperate. The 2022 Conference on Artificial Life. The 2022 Conference on Artificial Life, Online. https://doi.org/10.1162/isal_a_00489
Canino-Koning, R., Wiser, M. J., & Ofria, C. (2019). Fluctuating environments select for short-term phenotypic variation leading to long-term exploration. PLOS Computational Biology, 15(4), e1006445. https://doi.org/10/gk9zzw
Çatak, J., Ozilgen, M., & Yilmaz, B. (2018). Thermodynamic analysis of human respiratory (diaphragm) skeletal muscles. European Respiratory Journal, 52(suppl 62), PA2447. https://doi.org/10.1183/13993003.congress-2018.PA2447
Čejková, J., Banno, T., Hanczyc, M. M., & Štěpánek, F. (2017). Droplets As Liquid Robots. Artificial Life, 23(4), 528–549. https://doi.org/10/gcj4gc
Chan, B. W.-C. (2020). Lenia and Expanded Universe. 221–229. https://doi.org/10/gm3wrq
Chan, B. W.-C. (2019). Lenia: Biology of Artificial Life. Complex Systems, 28(3), 251–286. https://doi.org/10/ggf344
Chandler, C. H., Ofria, C., & Dworkin, I. (2013). Runaway Sexual Selection Leads to Good Genes. Evolution, 67(1), 110–119. https://doi.org/10/f4kgfb
Channon, A. (2003). Improving and Still Passing the ALife Test: Component-normalised Activity Statistics Classify Evolution in Geb As Unbounded. In R. K. Standish, M. A. Bedau, & H. A. Abbass (Eds.), Proceedings of the Eighth International Conference on Artificial Life (pp. 173–181). MIT Press. http://dl.acm.org/citation.cfm?id=860295.860326
Channon, A. (2001). Passing the ALife test: activity statistics classify evolution in Geb as unbounded. In J. Kelemen & P. Sosík (Eds.), Advances in Artificial Life (pp. 417–426). Springer Berlin Heidelberg.
Channon, A. (2019). Maximum Individual Complexity is Indefinitely Scalable in Geb. Artificial Life, 25, 134–144. https://doi.org/10.1162/artl_a_00285
Cheney, N., MacCurdy, R., Clune, J., & Lipson, H. (2014). Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding. ACM SIGEVOlution, 7(1), 11–23. https://doi.org/10/ggdr7m
Chow, S. S., Wilke, C. O., Ofria, C., Lenski, R. E., & Adami, C. (2004). Adaptive Radiation from Resource Competition in Digital Organisms. Science, 305(5680), 84–86. https://doi.org/10/cxhbw5
Clark, E., Hickinbotham, S., & Nellis, A. (n.d.). Research Using the Stringmol Artificial Chemistry. https://stringmol.york.ac.uk/rutsac_prez.pdf
Clune, J., Ofria, C., & Pennock, R. T. (2007). Investigating the Emergence of Phenotypic Plasticity in Evolving Digital Organisms. In F. Almeida e Costa, L. M. Rocha, E. Costa, I. Harvey, & A. Coutinho (Eds.), Advances in Artificial Life (Vol. 4648, pp. 74–83). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74913-4_8
Clune, J., Misevic, D., Ofria, C., Lenski, R. E., Elena, S. F., & Sanjuán, R. (2008). Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes. PLOS Computational Biology, 4(9), e1000187. https://doi.org/10/bjx63k
Clune, J., Goldsby, H. J., Ofria, C., & Pennock, R. T. (2011). Selective pressures for accurate altruism targeting: evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory. Proceedings of the Royal Society B: Biological Sciences, 278(1706), 666–674. https://doi.org/10/ddfgw2
Clune, J., Pennock, R. T., Ofria, C., & Lenski, R. E. (2012). Ontogeny Tends to Recapitulate Phylogeny in Digital Organisms. The American Naturalist, 180(3), E54–E63. https://doi.org/10/f36b9g
Cooper, T. F., & Ofria, C. (2002). Evolution of stable ecosystems in populations of digital organisms. Proceedings of the Eighth International Conference on Artificial Life, 227–232.
Corominas-Murtra, B., Seoane, L. F., & Solé, R. (2018). Zipf’s Law, unbounded complexity and open-ended evolution. Journal of The Royal Society Interface, 15(149), 20180395. https://doi.org/10.1098/rsif.2018.0395
Covert, A. W., Lenski, R. E., Wilke, C. O., & Ofria, C. (2013). Experiments on the role of deleterious mutations as stepping stones in adaptive evolution. Proceedings of the National Academy of Sciences, 110(34), E3171–E3178. https://doi.org/10/f48hqp
Crombach, A., & Hogeweg, P. (2009). Evolution of resource cycling in ecosystems and individuals. BMC Evolutionary Biology, 9(1), 122. https://doi.org/10/bgqt4p
Cully, A., Clune, J., Tarapore, D., & Mouret, J.-B. (2015). Robots that can adapt like animals. Nature, 521(7553), 503–507. https://doi.org/10/gdg46t
Dittrich, P., Ziegler, J., & Banzhaf, W. (2001). Artificial Chemistries—A Review. Artificial Life, 7(3), 225–275. https://doi.org/10.1162/106454601753238636