Spatial structure is hypothesized to be an important factor in the origin of life, wherein encapsulated chemical reaction networks came together to form systems capable adaptive complexification via Darwinian evolution. In this work, we use a computational model to investigate how different patterns of environmental connectivity influence the emergence of adaptive processes in simulated systems of self-amplifying networks of interacting chemical reactions (autocatalytic cycles, “ACs”). Specifically, we measured the propensity for adaptive dynamics to emerge in communities with nine distinct patterns of inter-AC interactions, across ten different patterns of environmental connectivity. We found that the pattern of connectivity can dramatically influence the emergence of adaptive processes; however, the effect of any particular spatial pattern varied across systems of ACs. Relative to a well-mixed (fully connected) environment, each spatial structure that we investigated amplified adaptive processes for at least one system of ACs and suppressed adaptive processes for at least one other system. Our findings suggest that there may be no single environment that universally promotes the emergence of adaptive processes in a system of interacting components (e.g., ACs). Instead, the ideal environment for amplifying (or suppressing) adaptive dynamics will depend on the particularities of the system.

Genetic programming systems often use large training sets to evaluate the quality of candidate solutions for selection, which is often computationally expensive. Down-sampling training sets has long been used to decrease the computational cost of evaluation in a wide range of application domains. More specifically, recent studies have shown that both random and informed down-sampling can substantially improve problem-solving success for GP systems that use the lexicase parent selection algorithm. We test whether these down-sampling techniques can also improve problem-solving success in the context of three other commonly used selection methods, fitness-proportionate, tournament, implicit fitness sharing plus tournament selection, across six program synthesis GP problems. We verified that down-sampling can significantly improve the problem-solving success for all three of these other selection schemes, demonstrating its general efficacy. We discern that the selection pressure imposed by the selection scheme does not interact with the down-sampling method. However, we find that informed down-sampling can improve problem solving success significantly over random down-sampling when the selection scheme has a mechanism for diversity maintenance like lexicase or implicit fitness sharing. Overall, our results suggest that down-sampling should be considered more often when solving test-based problems, regardless of the selection scheme in use.

Co-evolution is a powerful problem-solving approach. However, fitness evaluation in co-evolutionary algorithms can be computationally expensive, as the quality of an individual in one population is defined by its interactions with many (or all) members of one or more other populations. To accelerate co-evolutionary systems, we introduce phylogenyinformed interaction estimation, which uses runtime phylogenetic analysis to estimate interaction outcomes between individuals based on how their relatives performed against each other. We test our interaction estimation method with three distinct co-evolutionary systems: two systems focused on measuring problem-solving success and one focused on measuring evolutionary open-endedness. We find that phylogeny-informed estimation can substantially reduce the computation required to solve problems, particularly at the beginning of long-term evolutionary runs. Additionally, we find that our estimation method initially jump-starts the evolution of neural complexity in our open-ended domain, but estimation-free systems eventually “catch-up” if given enough time. Further refinements to these phylogeny-informed interaction estimation methods offer a promising path to reducing the computational cost of running co-evolutionary systems while maintaining their open-endedness.

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.