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.

Open-ended evolution – the presence of rich evolutionary dynamics that continuously produce novel, complex communities and species – is a key feature of the natural world. Understanding the conditions that enable open-endedness is a major challenge in artificial life and evolutionary computation. The MODES toolbox, consisting of metrics for detecting change, novelty, ecology, and complexity, is a promising approach for quantifying open-endedness. However, MODES has only been applied to a few systems so far, with limited opportunity for controlled experiments or cross-system comparisons. To address this gap, we implement a custom digital evolution platform (Evo-Sandbox) designed specifically for this purpose. Evo-Sandbox includes configurable modules for that can be combinatorially combined to create diverse environments. We investigate two diversity promoting mechanisms, fit-when-rare, and parasites, to test MODES across a range of conditions. Our experiments reveal that the regions of parameter space in which different hallmarks of open-endedness are maximized are non-intuitive, and that MODES is, in fact, a valuable tool for understanding the resulting behaviors.

Many open questions surround the processes that led to the evolutionary origin of mutualism between hosts and endosymbionts. In particular, large effect size mutations and coevolution have both been hypothesized to be important. Here we conduct in silico experiments using the Symbulation platform to explore these questions. We find that increasing mutation size and mutation rate both promote the evolution of mutualism, as does faster generational turnover within symbionts. Our results support the idea that large effect size mutations are important for the de novo evolution of mutualism. Indeed, follow-up mathematical modeling suggests that large regions of the parameter space where mutualism evolves can be explained purely by mutation size and rate. However, we observe that mutualism evolves in a wider region of parameter space than we would expect under this simple probabilistic model. We hypothesize that coevolutionary forces are responsible for this discrepancy, a hypothesis that is further corroborated by phylogenetic data showing that partners in the first mutualism are often themselves descended from mutualists. Ultimateley, we conclude that both mutation size and coevolution play a role in the evolution of mutualism. We anticipate that our findings will generalize to other systems featuring evolution along the parasitism-mutualism spectrum. Our work furthers efforts to predict host-endosymbiont coevolution.

Continuing improvements in computing hardware are poised to transform capabilities for in silico modeling of cross-scale phenomena underlying major open questions in evolutionary biology and artificial life, such as transitions in individuality, eco-evolutionary dynamics, and rare evolutionary events. Emerging ML/AI-oriented hardware accelerators, like the 850,000 processor CerebrasWafer Scale Engine (WSE), hold particular promise. However, many practical challenges remain in conducting informative evolution experiments that efficiently utilize these platforms’ large processor counts. Here, we focus on the problem of extracting phylogenetic information from agent-based evolution on the WSE platform. This goal drove significant refinements to decentralized in silico phylogenetic tracking, reported here. These improvements yield order-of-magnitude performance improvements. We also present an asynchronous island-based genetic algorithm (GA) framework forWSE hardware. Emulated and on-hardware GA benchmarks with a simple tracking-enabled agent model clock upwards of 1 million generations a minute for population sizes reaching 16 million agents. This pace enables quadrillions of agent replication events a day. We validate phylogenetic reconstructions from these trials and demonstrate their suitability for inference of underlying evolutionary conditions. In particular, we demonstrate extraction, from wafer-scale simulation, of clear phylometric signals that differentiate runs with adaptive dynamics enabled versus disabled. Together, these benchmark and validation trials reflect strong potential for highly scalable agent-based evolution simulation that is both efficient and observable. Developed capabilities will bring entirely new classes of previously intractable research questions within reach, benefiting further explorations within the evolutionary biology and artificial life communities across a variety of emerging high-performance computing platforms.

Identifying conditions that promote egalitarian major transitions, where unlike replicating units unite to form a higher-level unit, is an open problem with far-reaching implications. We propose that egalitarian major transitions can only begin in ecological communities that are conducive to them. To formalize this idea, we introduce the concept of “transition-ability”, which describes the extent to which a community is poised to undergo an egalitarian major transition. We hypothesize that transitionability is a property of ecological interaction networks, which represent the set of pairwise interactions among members of a community. Using a digital artificial ecology that simulates interactions between species based on a static interaction network, we test the transition-ability of interaction networks created by a range of graph-generation techniques, as well as some real-world ecological networks. To measure the extent to which a community is moving towards a major transition, we quantify the increase in community-level fitness relative to individual-level fitness across five different fitness proxies. We find that some network generation protocols produce more transitionable networks than others. In particular, interaction strengths (i.e. edge weights) have a substantial impact on transitionability, despite receiving low attention in the literature.

As digital evolution systems grow in scale and complexity, observing and interpreting their evolutionary dynamics will become increasingly challenging. Distributed and parallel computing, in particular, introduce obstacles to maintaining the high level of observability that makes digital evolution a powerful experimental tool. Phylogenetic analyses represent a promising tool for drawing inferences from digital evolution experiments at scale. Recent work has introduced promising techniques for decentralized phylogenetic inference in parallel and distributed digital evolution systems. However, foundational phylogenetic theory necessary to apply these techniques to characterize evolutionary dynamics is lacking. Here, we lay the groundwork for practical applications of distributed phylogenetic tracking in three ways: 1) we present an improved technique for reconstructing phylogenies from tunably-precise genome annotations, 2) we begin the process of identifying how the signatures of various evolutionary dynamics manifest in phylogenetic metrics, and 3) we quantify the impact of reconstruction-induced imprecision on phylogenetic metrics. We find that selection pressure, spatial structure, and ecology have distinct effects on phylogenetic metrics, although these effects are complex and not always intuitive. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully.

The problem of identifying conditions that enable major evolutionary transitions, in which distinct units come together to form a new higher level unit, is a complex and difficult topic spanning many disciplines. Here, we approach this problem from the perspective of the origin of life, which allows us to make the simplifying assumption that the lower-level units are not also evolving. This assumption lets us focus on identifying environmental factors that promote egalitarian major transitions in general and the origin of life specifically. To study this question, we build a simple artificial ecology model. We quantify major-transition-like dynamics using a maximum likelihood approach and a set of null models predicting the behavior of our system under various dynamics. Ultimately, we find that, even in a maximally simple artificial ecology model, we are able to observe evidence of community-level selection and thus the beginnings of a major evolutionary transition. The regions of parameter space that promote community-level selection vary based on species interactions but we observe consistent trends.

Endosymbiosis, symbiosis in which one symbiont lives inside another, is woven throughout the history of life and the story of its evolution. From the mitochondrion residing in almost every eukaryotic cell to the gut microbiome found in every human, endosymbiosis is a cornerstone of the biological processes that sustain life on Earth. While endosym-biosis is ubiquitous, many questions about its origins remain shrouded in mystery; one question in particular regards the general conditions and possible trajectories for its evolution. Modern science has hypothesized two possible pathways for the evolution of mutualistic endosymbiosis: one where an obligate antagonism is co-opted into an obligate mutualism (Co-Opted Antagonism Hypothesis), and one where a facultative mutualism evolves into an obligate mutualism (Black Queen Hypothesis). We investigated the viability of these pathways under different environmental conditions by expanding on the evolutionary agent-based system Symbulation. Specifically, we considered the impact of ectosymbiosis on de novo evolution of obligate mutualistic endosymbiosis. We found that introducing a facultative ectosymbiotic state allows endosym-biosis to evolve in a more diverse set of environmental conditions, while also decreasing the evolution of endosymbiosis in conditions where it can evolve independently.

Phylogenies provide direct accounts of the evolutionary trajectories behind evolved artifacts in genetic algorithm and artificial life systems. Phylogenetic analyses can also enable insight into evolutionary and ecological dynamics such as selection pressure and frequency-dependent selection. Traditionally, digital evolution systems have recorded data for phylogenetic analyses through perfect tracking where each birth event is recorded in a centralized data structure. This approach, however, does not easily scale to distributed computing environments where evolutionary individuals may migrate between a large number of disjoint processing elements. To provide for phylogenetic analyses in these environments, we propose an approach to enable phylogenies to be inferred via heritable genetic annotations rather than directly tracked. We introduce a “hereditary stratigraphy” algorithm that enables efficient, accurate phylogenetic reconstruction with tunable, explicit trade-offs between annotation memory footprint and reconstruction accuracy. In particular, we demonstrate an approach that enables estimation of the most recent common ancestor (MRCA) between two individuals with fixed relative accuracy irrespective of lineage depth while only requiring logarithmic annotation space complexity with respect to lineage depth. This approach can estimate, for example, MRCA generation of two genomes within 10% relative error with 95% confidence up to a depth of a trillion generations with genome annotations smaller than a kilobyte. We also simulate inference over known lineages, recovering up to 85.70% of the information contained in the original tree using 64-bit annotations.

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.

The concept of diversity has different definitions, usages, and nuances when looking from one field to another. Evolutionary biologists are primarily interested in the population dynamics that produce diversity, ecologists want to understand the maintenance and community-level effects of diversity, and evolutionary computation researchers want to exploit diversity to produce better and more varied solutions to real-world problems. In artificial life, we are particularly interested in understanding diversity as a critical component of natural systems in order to produce artificial ones that exhibit comparable open-ended dynamics. Here we begin to develop a framework to unite these views on diversity, with a goal of facilitating the transfer of ideas among these fields and formulating a consistent vocabulary.

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.

Do local conditions influence evolution’s ability to produce new traits? Biological data demonstrate that evolutionary processes can be profoundly influenced by local conditions. However, the evolution of novel traits has not been addressed in this context, owing in part to the challenges of performing the necessary experiments with natural organisms. We conduct in silico experiments with the Avida Digital Evolution Platform to address this question. We created eight different spatially heterogeneous environments and ran 100 replicates in each. Within each environment, we examined the distribution of locations where nine different focal traits first evolved. Using spatial statistics methods, we identified regions within each environment that had significantly elevated probabilities of containing the first organism with a given trait (i.e. hotspots of evolutionary potential). Having demonstrated the presence of many such hotspots, we explored three potential mechanisms that could drive the formation of these patterns: proximity of specific resources, variation in local diversity, and variation in the sequence of locations the members of an evolutionary lineage occupy. Resource proximity and local diversity appear to have minimal explanatory power. Lineage paths through space, however, show some promising preliminary trends. If we can understand the processes that create evolutionary hotspots, we will be able to craft environments that are more effective at evolving targeted traits. This capability would be useful both to evolutionary computation, and to efforts to guide biological evolution.