A common subject: Evolution through a computational lens. Two different communities: on the one hand, artificial life researchers use computational systems to understand emergent evolutionary processes and patterns such as complexity, robustness, evolvability and open-endedness; on the other hand, evolutionary bioinformatics researchers decipher patterns and processes in diverse domains of life on Earth using computational methods based on biological data. Both communities use simulations of living organisms but with different aims, objects, and methods, resulting in disjoint research corpuses. We propose Aevol 4b, an artificial life evolution simulator, and show that the data it produces can be successfully and interestingly processed using bioinformatics methods. This bridges the gap between the two fields and paves the way for fruitful exchanges between artificial life models and bioinformatic analysis methods.

As the field of Artificial Life advances and grows, we find ourselves in the midst of an increasingly complex ecosystem of software systems. Each system is developed to address particular research objectives, all unified under the common goal of understanding life. Such an ambitious endeavor begets a variety of algorithmic challenges. Many projects have solved some of these problems for individual systems, but these solutions are rarely portable and often must be re-engineered across systems. Here, we propose a community-driven process of developing standards for representing commonly used types of data across our field. These standards will improve software re-use across research groups and allow for easier comparisons of results generated with different artificial life systems. We began the process of developing data standards with two discussion-driven workshops (one at the 2018 Conference for Artificial Life and the other at the 2018 Congress for the BEACON Center for the Study of Evolution in Action). At each of these workshops, we discussed the vision for Artificial Life data standards, proposed and refined a standard for phylogeny (ancestry tree) data, and solicited feedback from attendees. In addition to proposing a general vision and framework for Artificial Life data standards, we release and discuss version 1.0.0 of the standards. This release includes the phylogeny data standard developed at these workshops and several software resources under development to support our proposed phylogeny standards framework.

When Artificial Life approaches are used with school pupils, it is generally to help them learn about the dynamics of living systems and/or their evolution. Here, we propose to use it to teach the scientific and experimental method, rather than biology. We experimented this alternative pedagogical usage during the 5 days internship of a young schoolboy – Quentin – with astonishing results. Indeed, not only Quentin easily grasped the principles of science and experiments but meanwhile he also collected very interesting results that shed a new light on the evolution of genome size and, more precisely, on genome streamlining. This article summarizes this success story and analyzes its results on both educational and scientific perspectives.