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.

Fine-scale evolutionary dynamics can be challenging to tease out when focused on broad brush strokes of whole populations over long time spans. We propose a suite of diagnostic metrics that operate on lineages and phylogenies in digital evolution experiments with the aim of improving our capacity to quantitatively explore the nuances of evolutionary histories in digital evolution experiments. We present three types of lineage measurements: lineage length, mutation accumulation, and phenotypic volatility. Additionally, we suggest the adoption of four phylogeny measurements from biology: depth of the most-recent common ancestor, phylogenetic richness, phylogenetic divergence, and phylogenetic regularity. We demonstrate the use of each metric on a set of two-dimensional, real-valued optimization problems under a range of mutation rates and selection strengths, confirming our intuitions about what they can tell us about evolutionary dynamics.

Gene duplications have been shown to promote evolvability in biology and in computational systems. We use digital evolution to explore why; that is, what characteristics of gene duplications increase evolutionary potential? Are duplications valuable because they inflate the effective mutation rate, generating increased amounts of genetic variation? Or is it that those mutations are clustered together? Or, is it that the mutations insert genetic material, providing evolution an easy technique to select for longer genomes? Does the value pertain to the information being duplicated in the genome? If so, is the full structure of duplicated code critical, or would the duplication of functional building blocks be valuable even if rearranged? Using the Avida Digital Evolution Platform, we experimentally tease apart these aspects in two qualitatively different environments: one where complex computational traits are directly selected, and another where those traits need to be regulated based on current environmental conditions. We confirm that gene duplications promote evolvability in both static and changing environments. Furthermore, we find that the primary value of gene duplications comes from their capacity to duplicate existing genetic information within a genome. Specifically, while duplications that randomize the order of genetic material are valuable, the most useful form of duplication also preserve the structure (and thus information content) of duplicated sequences.

Many effective and innovative survival mechanisms used by natural organisms rely on the capacity for phenotypic plasticity; that is, the ability of a genotype to alter how it is expressed based on the current environmental conditions. Understanding the evolution of phenotypic plasticity is an important step towards understanding the origins of many types of biological complexity, as well as to meeting challenges in evolutionary computation where dynamic solutions are required. Here, we leverage the Avida Digital Evolution Platform to experimentally explore the selective pressures and evolutionary pathways that lead to phenotypic plasticity. We present evolved lineages wherein unconditional traits tend to evolve first; next, imprecise forms of phenotypic plasticity often appear before optimal forms finally evolve. We visualize the phenotypic states traversed by evolved lineages across environments with differing rates of mutations and environmental change. We see that under all conditions, populations can fail to evolve phenotypic plasticity, instead relying on mutation-based solutions.