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.

Using the in silico experimental evolution platform Aevol, we evolved populations of digital organisms in conditions where a simple functional structure is best. Strikingly, we observed that in a large fraction of the simulations, organisms evolved a complex functional structure and that their complexity increased during evolution despite being a lot less fit than simple organisms in other populations. However, when submitted to a harsh mutational pressure, we observed that a significant proportion of complex individuals ended up with a simple functional structure. Our results suggest the existence of a complexity ratchet that is powered by epistasis and that cannot be beaten by selection. They also show that this ratchet can be overthrown by robustness because of the strong constraints it imposes on the coding capacity of the genome.

In this paper, we describe a new digital genetics model based on the Aevol artificial life simulator. Aevol is a computational platform designed to study populations of digital organisms evolving under various conditions. It has been extended in two directions. First, we have extended the genomic code from a binary one to a 4-base one, allowing for more realistic genomic sequence and easing the usage of Aevol as a benchmarking tool for comparative genomics. Second, we have replaced the Aevol continuous phenotype representation by a discrete one inspired by Fisher’s Geometric Model. By doing so, we will be able to validate Aevol results against population genetics theory.