Natural evolution and complex adaptations often surprise scientists. However, the creativity of evolution is not limited to the natural world, transcending any particular substrate. In the context of digital evolution, artificial organisms evolving in computational environments are also able to elicit surprise and wonder. Indeed, most digital evolution researchers can relate anecdotes highlighting how common it is for their algorithms to creatively subvert their expectations or intentions, expose unrecognized bugs in their code, produce unexpectedly potent adaptations, or engage in behaviors and outcomes uncannily convergent with ones found in nature. Such stories routinely reveal the surprise and creativity of evolution in these digital worlds, but they rarely fit into the standard scientific narrative and are treated as obstacles to be overcome rather than interesting results. Bugs are fixed, experiments are refocused, and one-off surprises become stories traded among researchers through lossy, inefficient and error-prone oral tradition. Moreover, to our knowledge, no collection of such anecdotes has been published before and many natural scientists do not recognize how lifelike digital organisms are and how natural their evolution can be. We have crowd-sourced the writing of a paper and collected first-hand reports from artificial life and evolutionary computation researchers, creating a written, fact-checked collection of entertaining and important stories. It serves to show that evolutionary surprise generalizes beyond the natural world, and may indeed be a universal property of all complex evolving systems.

When cooperating with others carries a direct fitness cost for the individual, natural selection should act against such behavior. However, cooperation is widespread across natural systems. Here we summarize and expand on a recently published work that highlights the importance a previously overlooked factor, the population shape. We used a combination of three in silico systems, Aevol (complex genomes, cooperation based on public good secretion), Aevol-lite (one-locus two-alleles, cooperation based on public good secretion) and CAevol (Prisoners dilemma with two pure strategies, cooperate and defect). In all three systems, higher level of cooperation evolved when populations lived in bulky worlds, a torus created by folding 100x100 quadrilateral grid, rather than slender ones, based on a 4x2500 grid. The result was not intuitive, and we able to discern the population dynamics only upon close examination of the full populations over time, in Aevol-lite. Populations are in dynamic equilibrium, constant emergence and extinction of cooperator patches. We show that slim populations effectively constrained the expansion and thus decreased the size these patches, leading to less cooperation on average. In the extended abstract we further discuss the implications of this work for natural systems, with populations embedded in different dimensions, populations as graphs, and also it with the work on infectious cooperation.