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
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
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
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
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
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
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
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.
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
Dolson, E., & Ofria, C. (2017). Spatial resource heterogeneity creates local hotspots of evolutionary potential. In C. Knibbe, G. Beslon, D. Parsons, D. Misevic, J. Rouzaud-Cornabas, N. Bredeche, S. Hassas, O. Simonin, & H. Soula (Eds.), ECAL 2017: The Fourteenth European Conference on Artificial Life (Vol. 29, pp. 122–129). MIT Press. https://doi.org/10.1162/isal_a_023
Dolson, E., Banzhaf, W., & Ofria, C. (2018). Applying Ecological Principles to Genetic Programming. In W. Banzhaf, R. S. Olson, W. Tozier, & R. Riolo (Eds.), Genetic Programming Theory and Practice XV (pp. 73–88). Springer International Publishing.
Dolson, E., Lalejini, A., Jorgensen, S., & Ofria, C. (2020). Interpreting the Tape of Life: Ancestry-based Analyses Provide Insights and Intuition about Evolutionary Dynamics. Artificial Life, 26(1), 1–22. https://direct.mit.edu/artl/article/26/1/58/93272/Interpreting-the-Tape-of-Life-Ancestry-Based
Dolson, E. L., Vostinar, A. E., Wiser, M. J., & Ofria, C. (2019). The MODES Toolbox: Measurements of Open-Ended Dynamics in Evolving Systems. Artificial Life, 25(1), 50–73. https://doi.org/10.1162/artl_a_00280
Dolson, E., Wiser, M. J., & Ofria, C. A. (2016). The Effects of Evolution and Spatial Structure on Diversity in Biological Reserves. In C. Gershenson, T. Froese, J. M. Siqueiros, W. Aguilar, E. J. Izquierdo, & H. Sayama (Eds.), Artificial Life XV: Proceedings of the Fifteenth International Conference on Artificial Life (pp. 434–440). MIT Press. https://doi.org/10/gktxx7
Elena, S. F., Wilke, C. O., Ofria, C., & Lenski, R. E. (2007). Effects of Population Size and Mutation Rate on the Evolution of Mutational Robustness. Evolution, 61(3), 666–674. https://doi.org/10/fvv6f6
Elsberry, W. R., Grabowski, L. M., Ofria, C., & Pennock, R. T. (2009). Cockroaches, drunkards, and climbers: Modeling the evolution of simple movement strategies using digital organisms. 2009 IEEE Symposium on Artificial Life, 92–99. https://doi.org/10/cwzcnx
Etcheverry, M., Moulin-Frier, C., & Oudeyer, P.-Y. (2020). Hierarchically organized latent modules for exploratory search in morphogenetic systems. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in neural information processing systems (Vol. 33, pp. 4846–4859). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2020/file/33a5435d4f945aa6154b31a73bab3b73-Paper.pdf
Fortuna, M. A., Zaman, L., Wagner, A. P., & Ofria, C. (2013). Evolving Digital Ecological Networks. PLOS Computational Biology, 9(3), e1002928. https://doi.org/10/f4vmd2
Fortuna, M. A., Zaman, L., Ofria, C., & Wagner, A. (2017). The genotype-phenotype map of an evolving digital organism. PLOS Computational Biology, 13(2), e1005414. https://doi.org/10/f9tgpr
Gerlee, P., & Anderson, A. R. A. (2007). An evolutionary hybrid cellular automaton model of solid tumour growth. Journal of Theoretical Biology, 246(4), 583–603. https://doi.org/10/dm5dqw
Goings, S., Clune, J., Ofria, C., & Pennock, R. T. (2004). Kin-Selection: The Rise and Fall of Kin-Cheaters. https://doi.org/10/gk9zzx
Goldsby, H. J., Dornhaus, A., Kerr, B., & Ofria, C. (2012). Task-switching costs promote the evolution of division of labor and shifts in individuality. Proceedings of the National Academy of Sciences, 109(34), 13686–13691. https://doi.org/10/f366dg
Goldsby, H. J., Knoester, D. B., Ofria, C., & Kerr, B. (2014). The Evolutionary Origin of Somatic Cells under the Dirty Work Hypothesis. PLOS Biology, 12(5), e1001858. https://doi.org/10/f55wvc
Goldsby, H. J., Cheng, B. H. C., McKinley, P. K., Knoester, D. B., & Ofria, C. A. (2008). Digital Evolution of Behavioral Models for Autonomic Systems. 2008 International Conference on Autonomic Computing, 87–96. https://doi.org/10/d468j8
Goldsby, H. J., Knoester, D. B., & Ofria, C. (2010). Evolution of division of labor in genetically homogenous groups. Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, 135–142. https://doi.org/10/ckd787