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.
Adami, C., Ofria, C., & Collier, T. C. (2000). Evolution of biological complexity. Proceedings of the National Academy of Sciences, 97(9), 4463–4468.
Armstrong, R. (2015). How do the origins of life sciences influence 21st century design thinking? 2–11.
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.
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.
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.
Birhane, A. (2021). The Impossibility of Automating Ambiguity. Artificial Life, 27(1), 44–61.
Biswas, R., Bryson, D., Ofria, C., & Wagner, A. (2014). Causes vs Benefits in the Evolution of Prey Grouping. 641–648.
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.
Bongard, J. C. (2013). Evolutionary robotics. Communications of the ACM, 56(8), 74–83.
Bongard, J. (2010). The Utility of Evolving Simulated Robot Morphology Increases with Task Complexity for Object Manipulation. Artificial Life, 16(3), 201–223.
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.
Brant, J. C., & Stanley, K. O. (2017). Minimal criterion coevolution: a new approach to open-ended search. 67–74.
Bryson, D. M., & Ofria, C. (2013). Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures. PLOS ONE, 8(12), e83242.
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.
Bull, L. (2021). On the Emergence of Intersexual Selection: Arbitrary Trait Preference Improves Female-Male Coevolution. Artificial Life, 27(1), 15–25.
Bull, L. (2021). Are Artificial Dendrites Useful in Neuro-Evolution? Artificial Life, 27(2), 75–79.
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.
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.
Čejková, J., Banno, T., Hanczyc, M. M., & Štěpánek, F. (2017). Droplets As Liquid Robots. Artificial Life, 23(4), 528–549.
Chan, B. W.-C. (2020). Lenia and Expanded Universe. 221–229.
Chan, B. W.-C. (2019). Lenia: Biology of Artificial Life. Complex Systems, 28(3), 251–286.
Chandler, C. H., Ofria, C., & Dworkin, I. (2013). Runaway Sexual Selection Leads to Good Genes. Evolution, 67(1), 110–119.
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.
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.
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.
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.
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.
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.
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.
Crombach, A., & Hogeweg, P. (2009). Evolution of resource cycling in ecosystems and individuals. BMC Evolutionary Biology, 9(1), 122.
Cully, A., Clune, J., Tarapore, D., & Mouret, J.-B. (2015). Robots that can adapt like animals. Nature, 521(7553), 503–507.
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.
Dolson, E., & Ofria, C. (2021). Digital Evolution for Ecology Research: A Review. Frontiers in Ecology and Evolution, 9, 750779.
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.
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.
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.
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.
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.
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.
Fernando, C., & Sojakka, S. (2003). Pattern recognition in a bucket. European Conference on Artificial Life, 588–597.
Fortuna, M. A., Zaman, L., Wagner, A. P., & Ofria, C. (2013). Evolving Digital Ecological Networks. PLOS Computational Biology, 9(3), e1002928.
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.
Gallicchio, C., & Micheli, A. (2021). Deep reservoir computing. Reservoir Computing, 77–95.
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.
Ghosh, S., Opala, A., Matuszewski, M., Paterek, T., & Liew, T. C. (2019). Quantum reservoir processing. Npj Quantum Information, 5(1), 1–6.