Evolution by natural selection is predicted to occur whenever and wherever three conditions are met: variation, differential fitness, and heritability [1]. Setting up these conditions in a computer enables true, open-ended digital evolution (sometimes resulting in adaptations never anticipated by the researcher [2]). Digital evolution has been used to study for example evolution of complex traits [3] and adaptive radiation [4].

Avida [5] is a widely used software platform for digital evolution, which can be compared to a virtual petri dish. It consists of a virtual grid filled with a collection of self-replicating computer programs – digital organisms. The organisms, also called ‘Avidians’, replicate by copying their genetic program. When they do so, there is a small risk that they will make an error, thus introducing a mutation in the offspring’s genome. Space limitation in the virtual world cause an evolutionary pressure for rapid replication, and Avidians effectively compete for access to computational power to increase their replication rate.

The virtual environment can be stacked with resources, which organisms can evolve the ability to consume. They consume resources by performing associated computations, or ‘tasks’, and are rewarded with extra computational power when they do so. By configuring each resource to create a byproduct when consumed, it is possible for cross-feeding to evolve in the population.

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1. Lewontin, R. C., 1970. The units of selection. Annual Review of Ecology and Systematics 1, 1–18.
2. Lehman, J., et al., 2018. The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities. In: Artificial Life Conference Proceedings. MIT Press, pp. 55–56.
3. Lenski, R. E., Ofria, C., Pennock, R. T., Adami, C., 2003. The evolutionary origin of complex features. Nature 423 (6936), 139–144. 
4. Chow, S. S., Wilke, C. O., Ofria, C., Lenski, R. E., Adami, C., 2004. Adaptive radiation from resource competition in digital organisms. Science 305, 84–86.
5. https://avida.devosoft.org/