When a new evolutionary dynamic is identified, researchers often struggle to understand its long-term effects on evolutionary outcomes. Evolutionary prediction is always challenging, as subtle nuances of dynamics can interact in unpredictable ways. Digital evolution systems, however, provide an empirical alternative to prediction: automated replay experiments can be conducted in large numbers to measure a real distribution of outcomes from a given starting point. Changes in distributions over time can help us understand the long-term implications of seemingly minor events during evolution. We apply this technique to “adaptive momentum”, a new framework that explains how phenomena like selective sweeps can temporarily weaken selection and enhance the likelihood of crossing deleterious fitness valleys. We show that deleterious mutations along the leading edge of a selective sweep can have an outsized influence on the evolutionary fate of a population. Indeed, we see that evolutionary potential to cross new deleterious valleys drastically increases during selective sweeps. Moreover, each valley crossing initiates a new sweep, increasing the potential for further discoveries; this increased potential subsides only once all sweeps have concluded. While we still have much to learn about both adaptive momentum and the role of history in evolution, this work identifies important evolutionary dynamics at play and hones our tools for further studies.

Scientists have long tried to predict evolutionary outcomes in order to design vaccines for next year’s diseases, stabilize endangered ecosystems, or make better choices in designing evolutionary algorithms. To predict, however, we must first be able to retroactively identify the key steps that determined the evolved state. Researchers have long examined the role of historical contingency in evolution; when do small, seemingly insignificant mutations substantially shift the probabilities of what traits or behaviors ultimately evolve? Practitioners of experimental evolution have recently begun to investigate this question using a new technique: analytic replay experiments. We can found many populations with a given genotype in order to measure the probability of a particular trait evolving from that starting point; we call this the “potentiation” of that genotype. Moving along a lineage, we can identify which mutations altered potentiation. Here we used digital organisms to conduct a high-resolution analysis of how individual mutations affected the potentiation of associative learning. We find that the probability of evolving associative learning can increase suddenly – even with a single mutation that appeared innocuous when it occurred. While there was no obvious signal to identify potentiating mutations as they arose, we were able to retrospectively identify mechanisms by which these mutations influenced subsequent evolution. Many of the most interesting and complex evolutionary adaptations that occur in nature are exceptionally rare. Here, we extend techniques for understanding these rare evolutionary events and the patterns and processes that produce them.

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.

The major evolutionary transition to multicellularity shifted the unit of selection from individual cells to multicellular organisms. Constituent cells must regulate their growth and cooperate to benefit the whole organism, even when such behaviors would have been maladaptive were they free living. Mutations that disrupt cellular cooperation can lead to various ailments, including physical deformities and cancer. Organisms therefore employ mechanisms to enforce cooperation, such as error correction, policing, and genetic robustness. We built a simulation to study this last mechanism under a range of evolutionary conditions. Specifically, we asked: How does genetic robustness against cellular cheating evolve in multicellular organisms? We focused on early multicellular organisms (with only one cell type) where cells must control their growth to avoid overwriting each other. In our model, unrestrained cells will outcompete restrained cells within an organism, but restrained cells alone will result in faster reproduction for the organism. Ultimately, we demonstrate a clear selective pressure for genetic robustness in multicellular organisms and show that this pressure increases with the total number of cells in the organism.

Understanding the structure and evolution of cognition is a topic of broad scientific interest. Computational substrates are ideal for conducting investigations into this topic because they can be incorporated in rapidly evolving Artificial Life systems and are easy to manipulate. However, design differences between currently existing digital systems make it difficult to identify which manipulations are responsible for broad patterns in evolved behavior. This is further confounded if we are trying to disentangle how multiple features interact. Here we systematically analyze components from two evolvable digital neural substrates (Recurrent Artificial Neural Networks (RNNs) and Markov brains) to develop a proof-of-concept for a comparative hybrid approach. We identified elements of the logic and memory storage architectures in each substrate, then altered and recombined properties of the original substrates to create hybrid substrates. In particular, we chose to investigate the differences between RNNs and Markov Brains relating to network sparsity, whether memory is discrete or continuous, and the basic logic operator in each substrate. We then tested the original substrates and the hybrids across a suite of distinct environments with different logic and memory requirements. While we observed trends across all three of the axes that we investigated, we identified discreteness of memory as an especially important determinant of performance across our test conditions. However, the specific effect of discretization varied by environment and whether the associated task relied on information integration. Our results demonstrate that the comparative hybrid approach can identify structural components that enable cognition and facilitate task performance across multiple computational structures.

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.

Sexual selection is a powerful yet poorly understood evolutionary force. Research into sexual selection, whether biological, computational, or mathematical, has tended to take a top-down approach studying complex natural systems. Many simplifying assumptions must be made in order to make these systems tractable, but it is unclear if these simplifications result in a system which still represents natural ecological and evolutionary dynamics. Here, we take a bottom-up approach in which we construct simple computational systems from subsets of biologically plausible components and focus on examining the underlying dynamics resulting from the interactions of those components. We use this method to investigate sexual selection in general and the sexy sons theory in particular. The minimally necessary components are therefore genomes, genome-determined displays and preferences, and a process capable of overseeing parent selection and mating. We demonstrate the efficacy of our approach (i.e we observe the evolution of female preference) and provide support for sexy sons theory, including illustrating the oscillatory behavior that developed in the presence of multiple costly display traits.

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.

Quorum sensing is a ubiquitous strategy in which bacteria are able to sense the presence of others via the density of a secreted molecule. Vibrio harveyi is one such bacterial species that uses quorum sensing to control a public goods cooperation strategy. As with all cooperative strategies, this strategy is at risk of cheating organisms ousting cooperators. Using the platform Empirical, we first replicated the results from a wetlab experiment and then determined the effects of population structure and resource availability on the de novo evolution, short-term, and long-term stability of a quorum sensing-controlled public goods strategy. We found that environments that enabled pre-existing cooperators to remain stable were not always the same environments in which cooperation could evolve de novo. Specifically, cooperation was able to persist in the short term in semi-structured populations with low resource levels, but not be maintained over long evolutionary time scales.

A major area of investigation in evolutionary biology focuses on understanding how intelligent behaviors first evolved. We have evidence in the fossil record that demonstrates an apparent increase in the upper bounds of organismal complexity over time, but the levels of intelligence displayed by those organisms is less clear. For example, the progression of behaviors registered in trace and other fossils from the Ediacaran period have inspired intense speculation as to the cognitive capacity of animals leading up to the Cambrian Explosion. While it is challenging to get a more detailed window into what actually transpired hundreds of millions of years ago, computational Artificial Life techniques allow us to conduct empirical studies under analogous conditions and examine the patterns by which intelligent behaviors arise. In a series of experiments using the Avida platform, we evolved digital organisms with simple sensory and locomotory potential that were capable of increasingly complex cognitive abilities, spanning from efficient patch harvesting to associative learning and nonelemental learning. The patterns of the evolutionary sequences of these organisms are reminiscent of those found in Precambrian fossils, and allow us to start refining our ideas about the evolutionary origins of intelligence.

Cooperation is a defining attribute of life as we know it, from the delicate interactions of intracellular components to social behavior in groups. However, defection and exploitation are at least as ubiquitous. Evolutionary game theory is a successful tool for investigating how cooperation may be maintained despite large advantages for defection. The Prisoners Dilemma is one such game where spatial structure can maintain cooperation, but only if the benefit-to-cost ratio (b/c) is greater than some threshold, which appears to be the average number of neighbors (k). However, this inequality was tested only for regular spatial and irregular non-spatial networks. In this paper, we use networks in Cartesian space that are based on radii of interactions. We investigate whether the b/c > k threshold holds for these irregular spatial networks, and we use a much broader range of k than previously studied. We find that this rule, and other related inequalities, hold well for the larger radii even when there is noise in the expected neighborhood size. As the expected neighborhood size increases, so does the variation in the empirical edge distribution. However, the variation in the threshold for cooperation decreases. This paper is a first step in a broad investigation of how uncertainty affects the outcome of game theoretic simulations.

Fitness landscapes are visual metaphors that appeal to our intuition for real-world landscapes to help us understand how populations evolve. The object inspiring the metaphor is better described as a networks composed of all possible genotypes, but they are frequently simplified to a surface where the fitness of each genotype is represented by elevation. Selection drives evolving populations to ascend the landscape until they are dominated by genotypes from which no further beneficial mutations are likely, known as a peak. However, by allowing for environmental change, former peaks can vanish, forcing populations to resume adapting. To explore how changing environments affect adaptation, we used the digital evolution platform, Avida, wherein we could manipulate the organisms’ environment as they are subject to natural evolutionary forces. We found that transient exposure to alternate environments frequently resulted in more fit genotypes. Negative-frequency-dependent environments, in particular, yielded strong fitness benefits after returning to the original environment. Furthermore, we explored how such environmental change could yield adaptive benefits via valley crossing and how such knowledge could be exploited in systems where improving the rate of adaption is beneficial.

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.

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.

Ecological Speciation is the development of reproductive isolation as a result of divergent adaptation to different environments. As populations diverge, post-zygotic isolation effects such as low hybrid fitness and zygotic inviability are expected to become increasingly dominant. However, for genetically similar incipient species, post-zygotic effects may not be sufficient to enforce a reduction in gene-flow. Models of allopatric speciation predict that pre-mating isolation may play an important role in reinforcing barriers between species, regardless of genetic incompatibilities. However, evidence for these models is mixed, and remains controversial. In this paper, we examine the extent to which pre-mating isolation resulting from divergent sexually-selected traits is sufficient to generate incipient species. We evolved populations of sexually-reproducing digital organisms that use sexual selection to choose their mates. These populations are then divided, and each half allowed to adapt to divergent environmental conditions (allopatry). We then reunited these populations for a single round of mating and measured the rate of hybridization. We found that sexual selection significantly reduces the number of hybrid matings between populations. Further, we found that post-zygotic effects were only minimally present, despite adaptation to distinct environments, and that there was little difference in both pre-mating and post-zygotic effects between distinct sets of environments. We conclude that sexual selection is a strong force for generating incipient species, even while post-zygotic effects have minimal impact.