Professional Works

Operations over data streams typically hinge on efficient mechanisms to aggregate or summarize history on a rolling basis. For high-volume data steams, it is critical to manage state in a manner that is fast and memory efficient — particularly in resource-constrained or real-time contexts. Here, we address the problem of extracting a fixed-capacity, rolling subsample from a data stream. Specifically, we explore “data stream curation” strategies to fulfill requirements on the composition of sample time points retained. Our “DStream” suite of algorithms targets three temporal coverage criteria: (1) steady coverage, where retained samples should spread evenly across elapsed data stream history; (2) stretched coverage, where early data items should be proportionally favored; and (3) tilted coverage, where recent data items should be proportionally favored. For each algorithm, we prove worst-case bounds on rolling coverage quality. We focus on the more practical, application-driven case of maximizing coverage quality given a fixed memory capacity. As a core simplifying assumption, we restrict algorithm design to a single update operation: writing from the data stream to a calculated buffer site — with data never being read back, no metadata stored (e.g., sample timestamps), and data eviction occurring only implicitly via overwrite. Drawing only on primitive, low-level operations and ensuring full, overhead-free use of available memory, this “DStream” framework ideally suits domains that are resource-constrained, performance-critical, and fine-grained (e.g., individual data items as small as single bits or bytes). The proposed approach supports O(1) data ingestion via concise bit-level operations. To further practical applications, we provide plug-and-play open-source implementations targeting both scripted and compiled application domains.

Moreno M. A., Zaman L., & Dolson E. (2024). Structured Downsampling for Fast, Memory-efficient Curation of Online Data Streams. arXiv preprint arXiv:2409.06199. https://doi.org/10.48550/arXiv.2409.06199

• manuscript source via GitHub
• software repository via GitHub
• software package via PyPI

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 Cerebras Wafer Scale Engine (WSE), hold particular promise. However, 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 for WSE 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. 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.

Moreno, M. A., Yang, C., Dolson, E., & Zaman, L. (2024). Trackable Agent-based Evolution Models at Wafer Scale. In The 2024 Conference on Artificial Life. MIT Press. https://doi.org/10.1162/isal_a_00830

• manuscript source via GitHub
• kernel software via GitHub
• data via Open Science Framework ❋
• conference slides via Google Slides

Continuing generation of novelty, complexity, and adaptation are well-established as core aspects of open-ended evolution. However, the manner in which these phenomena relate remains an area of great theoretical interest. It is yet to be firmly established to what extent these phenomena are coupled and by what means they interact. In this work, we track the co-evolution of novelty, complexity, and adaptation in a case study from a simulation system designed to study the evolution of digital multicellularity. In this case study, we describe ten qualitatively distinct multicellular morphologies, several of which exhibit asymmetrical growth and distinct life stages. We contextualize the evolutionary history of these morphologies with measurements of complexity and adaptation. Our case study suggests a loose, sometimes divergent, relationship can exist among novelty, complexity, and adaptation.

Matthew Andres Moreno, Santiago Rodriguez Papa and Charles Ofria. 2021. Case Study of Novelty, Complexity, and Adaptation in a Multicellular System. OEE4: The Fourth Workshop on Open-Ended Evolution. https://doi.org/10.48550/arXiv.2405.07241

• extended preprint via arXiv ☠️
• supplement via Open Science Framework ❋
• data via Open Science Framework ❋
• source code via GitHub
• web demo
• presentation via YouTube
• slides
• LaTeX source via GitHub

Evolutionary transitions occur when previously-independent replicating entities unite to form more complex individuals. Such transitions have profoundly shaped natural evolutionary history and occur in two forms: fraternal transitions involve lower-level entities that are kin (e.g., transitions to multicellularity or to eusocial colonies), while egalitarian transitions involve unrelated individuals (e.g., the origins of mitochondria). The necessary conditions and evolutionary mechanisms for these transitions to arise continue to be fruitful targets of scientific interest. Here, we examine a range of fraternal transitions in populations of open-ended self-replicating computer programs. These digital cells were allowed to form and replicate kin groups by selectively adjoining or expelling daughter cells. The capability to recognize kin-group membership enabled preferential communication and cooperation between cells. We repeatedly observed group-level traits that are characteristic of a fraternal transition. These included reproductive division of labor, resource sharing within kin groups, resource investment in offspring groups, asymmetrical behaviors mediated by messaging, morphological patterning, and adaptive apoptosis. We report eight case studies from replicates where transitions occurred and explore the diverse range of adaptive evolved multicellular strategies.

Moreno MA and Ofria C (2022) Exploring Evolved Multicellular Life Histories in a Open-Ended Digital Evolution System. Front. Ecol. Evol. 10:750837. doi: 10.3389/fevo.2022.750837

• data via Open Science Framework ❋
• source code via GitHub
• web demo
• supplementary material via frontiers
• presentation at BEACON Congress 2020 via YouTube
• slides via Google Slides
• LaTeX source via GitHub

Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution, and have been hypothesized to influence phylogenetic structure. For instance, they can help explain natural history, steer behavior of contemporary evolving populations, and influence efficacy of application-oriented evolutionary optimization. Likewise, in inquiry-oriented artificial life systems, these drivers constitute key building blocks for open-ended evolution. Here, we set out to assess (1) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure, (2) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure, and (3) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Further, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers’ phylometric signatures from each other and to appropriately normalize phylogenetic metrics. With such work, phylogenetic analysis could provide a versatile toolkit to study large-scale evolving populations.

Matthew Andres Moreno, Santiago Rodriguez-Papa, Emily Dolson; Ecology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure. Artif Life 2025; 31 (2): 129–152. doi: https://doi.org/10.1162/artl_a_00470

• manuscript source via GitHub
• code and notebooks via GitHub
• data via Open Science Framework ❋

Contemporary bioinformatics has seen in profound new visibility into the composition, structure, and history of the natural world around us. Arguably, the central pillar of bioinformatics is phylogenetics – the study of hereditary relatedness among organisms. Insight from phylogenetic analysis has touched nearly every corner of biology. Examples range across natural history, population genetics and phylogeography, conservation biology, public health, medicine, in vivo and in silico experimental evolution, application-oriented evolutionary algorithms, and beyond. High-throughput genetic and phenotypic data has realized groundbreaking results, in large part, through conjunction with open-source software used to process and analyze it. Indeed, the preceding decades have ushered in a flourishing ecosystem of bioinformatics software applications and libraries. Over the course of its nearly fifteen-year history, the DendroPy library for phylogenetic computation in Python has established a generalist niche in serving the bioinformatics community. Here, we report on the recent major release of the library, DendroPy version 5. The software release represents a major milestone in transitioning the library to a sustainable long-term development and maintenance trajectory. As such, this work positions DendroPy to continue fulfilling a key supporting role in phyloinformatics infrastructure.

Moreno, M. A., Holder, M. T., & Sukumaran, J. (2024). DendroPy 5: a mature Python library for phylogenetic computing. Journal of Open Source Software, 9(101), 6943, https://doi.org/10.21105/joss.06943

• repository via GitHub
• documentation via GitHub Pages
• pre-review via GitHub
• review via GitHub
• source archive via Zenodo z

Empirical is a C++ library designed to promote open science and facilitate the development of scientific software that is efficient, reliable, and easily distributable to researchers and non-experts alike. Specifically, the library sets out to fulfill the following goals:

1. Utility: Empirical tools streamline common scientific computing tasks such as configuration, end-to-end data management, and mathematical manipulations.
2. Efficiency: Empirical implements general-purpose data structures and algorithms that emphasize computational efficiency to support scientific computing workloads.
3. Reliability: Empirical provides sophisticated debug-mode instrumentation including audited memory management and safety-checked versions of standard library containers.
4. Distributability: Empirical is highly portable, uses common data formats, and facilitates compile-to-web app development with object-oriented bindings for Emscripten/WebAssembly GUI elements, all with the goal of building broadly accessible scientific software.

Vostinar, A., Lalejini, A., Ofria, C., Dolson, E., & Moreno, M.A. (2024). Empirical: A scientific software library for research, education, and public engagement. Journal of Open Source Software, 9(98), 6617, https://doi.org/10.21105/joss.06617

• repository via GitHub
• documentation via ReadTheDocs 📖
• Built with Empirical gallery via ReadTheDocs 📖
• source archive via Zenodo z
• pre-review via GitHub
• review via GitHub

Genetic programming and artificial life systems commonly use tag matching to decide interactions between system components. However, the implications of criteria used to determine affinity between tags with respect evolutionary dynamics have not been directly studied. We investigate differences between tag-matching criteria with respect to geometric constraint and variation generated under mutation. In experiments, we find that tag-matching criteria can influence the rate of adaptive evolution and the quality of evolved solutions. Better understanding of the geometric, variational, and evolutionary properties of tag-matching criteria will facilitate more effective incorporation of tag matching into genetic programming and artificial life systems. By showing that tag-matching criteria influence connectivity patterns and evolutionary dynamics, our findings also raise fundamental questions about the properties of tag-matching systems in nature.

Moreno, M.A., Lalejini, A. & Ofria, C. Matchmaker, matchmaker, make me a match: geometric, variational, and evolutionary implications of criteria for tag affinity. Genet Program Evolvable Mach 24, 4 (2023). https://doi.org/10.1007/s10710-023-09448-0

• data via Open Science Framework ❋
• source code via GitHub
• source code via GitHub
• LaTeX source via GitHub
• twitter thread

Digital evolution systems instantiate evolutionary processes over populations of virtual agents in silico. These programs can serve as rich experimental model systems. Insights from digital evolution experiments expand evolutionary theory, and can often directly improve heuristic optimization techniques . Perfect observability, in particular, enables in silico experiments that would be otherwise impossible in vitro or in vivo. Notably, availability of the full evolutionary history (phylogeny) of a given population enables very powerful analyses.

As a slow but highly parallelizable process, digital evolution will benefit greatly by continuing to capitalize on profound advances in parallel and distributed computing, particularly emerging unconventional computing architectures. However, scaling up digital evolution presents many challenges. Among these is the existing centralized perfect-tracking phylogenetic data collection model, which is inefficient and difficult to realize in parallel and distributed contexts. Here, we implement an alternative approach to tracking phylogenies across vast and potentially unreliable hardware networks.

Moreno M.A., Dolson, E., & Ofria, C. (2022). hstrat: a Python Package for phylogenetic inference on distributed digital evolution populations. Journal of Open Source Software, 7(80), 4866, https://doi.org/10.21105/joss.04866

• repository via GitHub
• documentation via ReadTheDocs 📖
• source archive via Zenodo z
• pre-review via GitHub
• review via GitHub

Evolutionary transitions occur when previously-independent replicating entities unite to form more complex individuals. Such transitions have profoundly shaped natural evolutionary history and occur in two forms: fraternal transitions involve lower-level entities that are kin (e.g., transitions to multicellularity or to eusocial colonies), while egalitarian transitions involve unrelated individuals (e.g., the origins of mitochondria). The necessary conditions and evolutionary mechanisms for these transitions to arise continue to be fruitful targets of scientific interest. Here, we examine a range of fraternal transitions in populations of open-ended self-replicating computer programs. These digital cells were allowed to form and replicate kin groups by selectively adjoining or expelling daughter cells. The capability to recognize kin-group membership enabled preferential communication and cooperation between cells. We repeatedly observed group-level traits that are characteristic of a fraternal transition. These included reproductive division of labor, resource sharing within kin groups, resource investment in offspring groups, asymmetrical behaviors mediated by messaging, morphological patterning, and adaptive apoptosis. We report eight case studies from replicates where transitions occurred and explore the diverse range of adaptive evolved multicellular strategies.

Moreno MA and Ofria C (2022) Exploring Evolved Multicellular Life Histories in a Open-Ended Digital Evolution System. Front. Ecol. Evol. 10:750837. doi: 10.3389/fevo.2022.750837

• data via Open Science Framework ❋
• source code via GitHub
• web demo
• supplementary material via frontiers
• presentation at BEACON Congress 2020 via YouTube
• slides via Google Slides
• LaTeX source via GitHub

We introduce and experimentally demonstrate the utility of tag-based genetic regulation, a new genetic programming (GP) technique that allows programs to dynamically adjust which code modules to express. Tags are evolvable labels that provide a flexible mechanism for referencing code modules. Tag-based genetic regulation extends existing tag-based naming schemes to allow programs to “promote” and “repress” code modules in order to alter expression patterns. This extension allows evolution to structure a program as a gene regulatory network where modules are regulated based on instruction executions. We demonstrate the functionality of tag-based regulation on a range of program synthesis problems. We find that tag-based regulation improves problem-solving performance on context-dependent problems; that is, problems where programs must adjust how they respond to current inputs based on prior inputs. Indeed, the system could not evolve solutions to some context-dependent problems until regulation was added. Our implementation of tag-based genetic regulation is not universally beneficial, however. We identify scenarios where the correct response to a particular input never changes, rendering tag-based regulation an unneeded functionality that can sometimes impede adaptive evolution. Tag-based genetic regulation broadens our repertoire of techniques for evolving more dynamic genetic programs and can easily be incorporated into existing tag-enabled GP systems.

Lalejini, A., Moreno, M.A. & Ofria, C. Tag-based regulation of modules in genetic programming improves context-dependent problem solving. Genet Program Evolvable Mach 22, 325–355 (2021). https://doi.org/10.1007/s10710-021-09406-8

• data via Open Science Framework ❋
• source code via GitHub

The emergence of new replicating entities from the union of simpler entities characterizes some of the most profound events in natural evolutionary history. Such transitions in individuality are essential to the evolution of the most complex forms of life. Thus, understanding these transitions is critical to building artificial systems capable of open-ended evolution. Alas, these transitions are challenging to induce or detect, even with computational organisms. Here, we introduce the DISHTINY (Distributed Hierarchical Transitions in Individuality) platform, which provides simple cell-like organisms with the ability and incentive to unite into new individuals in a manner that can continue to scale to subsequent transitions. The system is designed to encourage these transitions so that they can be studied: organisms that coordinate spatiotemporally can maximize the rate of resource harvest, which is closely linked to their reproductive ability. We demonstrate the hierarchical emergence of multiple levels of individuality among simple cell-like organisms that evolve parameters for manually designed strategies. During evolution, we observe reproductive division of labor and close cooperation among cells, including resource-sharing, aggregation of resource endowments for propagules, and emergence of an apoptosis response to somatic mutation. Many replicate populations evolved to direct their resources toward low-level groups (behaving like multicellular individuals), and many others evolved to direct their resources toward high-level groups (acting as larger-scale multicellular individuals).

Matthew Andres Moreno, Charles Ofria; Toward Open-Ended Fraternal Transitions in Individuality. Artif Life 2019; 25 (2): 117–133. doi: https://doi.org/10.1162/artl_a_00284

• data via Open Science Framework ❋
• how-to tutorials via Open Science Framework ❋
• source code via GitHub
• web demo
• presentation via YouTube
• slides
• LaTeX source via GitHub

The exocyst, a conserved, octameric protein complex, helps mediate secretion at the plasma membrane, facilitating specific developmental processes that include control of root meristem size, cell elongation, and tip growth. A genetic screen for second-site enhancers in Arabidopsis identified NEW ENHANCER of ROOT DWARFISM1 (NERD1) as an exocyst interactor. Mutations in NERD1 combined with weak exocyst mutations in SEC8 and EXO70A1 result in a synergistic reduction in root growth. Alone, nerd1 alleles modestly reduce primary root growth, both by shortening the root meristem and by reducing cell elongation, but also result in a slight increase in root hair length, bulging, and rupture. NERD1 was identified molecularly as At3g51050, which encodes a transmembrane protein of unknown function that is broadly conserved throughout the Archaeplastida. A functional NERD1–GFP fusion localizes to the Golgi, in a pattern distinct from the plasma membrane-localized exocyst, arguing against a direct NERD1–exocyst interaction. Structural modeling suggests the majority of the protein is positioned in the lumen, in a β-propeller-like structure that has some similarity to proteins that bind polysaccharides. We suggest that NERD1 interacts with the exocyst indirectly, possibly affecting polysaccharides destined for the cell wall, and influencing cell wall characteristics in a developmentally distinct manner.

Rex A Cole, Valera V Peremyslov, Savannah Van Why, Ibrahim Moussaoui, Ann Ketter, Renee Cool, Matthew Andres Moreno, Zuzana Vejlupkova, Valerian V Dolja, John E Fowler, A broadly conserved NERD genetically interacts with the exocyst to affect root growth and cell expansion, Journal of Experimental Botany, Volume 69, Issue 15, 10 July 2018, Pages 3625–3637, https://doi.org/10.1093/jxb/ery162

• poster
• slides

The structure of relatedness among members of an evolved population tells much of its evolutionary history. In application-oriented evolutionary computation (EC), such phylogenetic information can guide algorithm selection and tuning. Although traditional direct tracking approaches provide the perfect phylogenetic record, sexual recombination complicates management and analysis of this data. Taking inspiration from biological science, this work explores a reconstruction-based approach that uses end-state genetic information to estimate phylogenetic history after the fact. We apply recently-developed “hereditary stratigraphy” genome annotations to lineages with sexual recombination to design devices germane to species phylogenies and gene trees. As shown through a series of validation experiments, proposed instrumentation can discern genealogical history, population size changes, and selective sweeps. Fully decentralized by nature, these methods afford new observability at scale, in particular, for distributed EC systems. Such capabilities anticipate continued growth of computational resources available to EC. Accompanying open source software aims to expedite application of reconstruction-based phylogenetic analysis where pertinent.

Moreno, M.A. (2024). Methods for Rich Phylogenetic Inference Over Distributed Sexual Populations. In: Winkler, S., Trujillo, L., Ofria, C., Hu, T. (eds) Genetic Programming Theory and Practice XX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-8413-8_7

• data via Open Science Framework ❋
• source code via GitHub
• hstrat library code via GitHub
• conference slides via Google Slides
• presentation via YouTube
• LaTeX source via GitHub

Phylogenies (ancestry trees) tell the evolutionary history of an evolving population. In evolutionary computing, phylogenies reveal how evolutionary algorithms steer populations through a search space by illuminating the step-by-step evolution of solutions. To date, phylogenetic analyses have almost exclusively been applied in post hoc analyses of evolutionary algorithms for performance tuning and research. Here, we apply phylogenetic information at runtime to augment parent selection procedures that use training sets to assess candidate solution quality. We propose phylogeny-informed fitness estimation, thinning a fraction of costly training case evaluations by substituting the fitness profiles of near relatives as a heuristic estimate. We evaluate phylogeny-informed fitness estimation in the context of the down-sampled lexicase and cohort lexicase selection algorithms on two diagnostic analyses and four genetic programming (GP) problems. Our results indicate that phylogeny-informed fitness estimation can mitigate the drawbacks of down-sampled lexicase, improving diversity maintenance and search space exploration. However, the extent to which phylogeny-informed fitness estimation improves problem-solving success for GP varies by problem, subsampling method, and subsampling level. This work serves as an initial step toward improving evolutionary algorithms by exploiting runtime phylogenetic analysis.

Lalejini, A., Moreno, M.A., Hernandez, J.G., Dolson, E. (2024). Phylogeny-Informed Fitness Estimation for Test-Based Parent Selection. In: Winkler, S., Trujillo, L., Ofria, C., Hu, T. (eds) Genetic Programming Theory and Practice XX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-8413-8_13

• data via Open Science Framework ❋
• source code via Zenodo
• supplemental material

Computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestry-tracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via non-coding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-of-magnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

M. A. Moreno, A. Ranjan, E. Dolson and L. Zaman, “Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking,” 2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), Trondheim, Norway, 2025, pp. 1-9, doi: 10.1109/ALIFE-CIS64968.2025.10979833.

• manuscript source via GitHub
• code and notebooks via GitHub
• data via Open Science Framework ❋
• conference slides via Google Slides
• conference poster via Google Slides

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 Cerebras Wafer Scale Engine (WSE), hold particular promise. However, 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 for WSE 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. 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.

Moreno, M. A., Yang, C., Dolson, E., & Zaman, L. (2024). Trackable Agent-based Evolution Models at Wafer Scale. In The 2024 Conference on Artificial Life. MIT Press. https://doi.org/10.1162/isal_a_00830

• manuscript source via GitHub
• kernel software via GitHub
• data via Open Science Framework ❋
• conference slides via Google Slides

A phylogeny describes the evolutionary history of an evolving population. Evolutionary search algorithms can perfectly track the ancestry of candidate solutions, illuminating a population’s trajectory through the search space. However, phylogenetic analyses are typically limited to post-hoc studies of search performance. We introduce phylogeny-informed subsampling, a new class of subsampling methods that exploit runtime phylogenetic analyses for solving test-based problems. Specifically, we assess two phylogeny-informed subsampling methods – individualized random subsampling and ancestor-based subsampling – on three diagnostic problems and ten genetic programming (GP) problems from program synthesis benchmark suites. Overall, we found that phylogeny-informed subsampling methods enable problem-solving success at extreme subsampling levels where other subsampling methods fail. For example, phylogeny-informed subsampling methods more reliably solved program synthesis problems when evaluating just one training case per-individual, per-generation. However, at moderate subsampling levels, phylogeny-informed subsampling generally performed no better than random subsampling on GP problems. Our diagnostic experiments show that phylogeny-informed subsampling improves diversity maintenance relative to random subsampling, but its effects on a selection scheme’s capacity to rapidly exploit fitness gradients varied by selection scheme. Continued refinements of phylogeny-informed subsampling techniques offer a promising new direction for scaling up evolutionary systems to handle problems with many expensive-to-evaluate fitness criteria.

Alexander Lalejini, Marcos Sanson, Jack Garbus, Matthew Andres Moreno, and Emily Dolson. 2024. Runtime phylogenetic analysis enables extreme subsampling for test-based problems. In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO ‘24). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3638530.3654208

• data 1 via Open Science Framework ❋
• data 2 via Open Science Framework ❋
• source code via Zenodo
• supplemental material

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.

Moreno, M. A., Dolson, E., & Rodriguez-Papa, S. (2023). Toward Phylogenetic Inference of Evolutionary Dynamics at Scale. In The 2023 Conference on Artificial Life. MIT Press. https://doi.org/10.1162/isal_a_00694

• data via Open Science Framework ❋
• source code via GitHub
• hstrat library code via GitHub
• artificial life conference slides via Google Slides
• evolution conference presentation via YouTube
• evolution conference slides via Google Slides
• LaTeX source via GitHub

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.

Moreno, M. A., Dolson, E., & Ofria, C. (2022). Hereditary Stratigraphy: Genome Annotations to Enable Phylogenetic Inference over Distributed Populations. In The 2022 Conference on Artificial Life. MIT Press. https://doi.org/10.1162/isal_a_00550

• data via Open Science Framework ❋
• source code via GitHub
• hstrat library code via GitHub
• web demo 1
• web demo 2
• presentation via YouTube
• slides via Google Slides
• LaTeX source via GitHub

We present AutoMap, a pair of methods for automatic generation of evolvable genotype-phenotype mappings. Both use an artificial neural network autoencoder trained on phenotypes harvested from fitness peaks as the basis for a genotype-phenotype mapping. In the first, the decoder segment of a bottlenecked autoencoder serves as the genotype-phenotype mapping. In the second, a denoising autoencoder serves as the genotype-phenotype mapping. Automatic generation of evolvable genotype-phenotype mappings are demonstrated on the n-legged table problem, a toy problem that defines a simple rugged fitness landscape, and the Scrabble string problem, a more complicated problem that serves as a rough model for linear genetic programming. For both problems, the automatically generated genotype-phenotype mappings are found to enhance evolvability.

Matthew Andres Moreno, Wolfgang Banzhaf, and Charles Ofria. 2018. Learning an evolvable genotype-phenotype mapping. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ‘18). Association for Computing Machinery, New York, NY, USA, 983–990. https://doi.org/10.1145/3205455.3205597

• data via Open Science Framework ❋
• how-to tutorials via Open Science Framework ❋
• source code for n-legged table experiments via GitHub
• source code for Scrabble experiments via GitHub
• blog post
• presentation via YouTube
• slides
• LaTeX source via GitHub

Continuing generation of novelty, complexity, and adaptation are well-established as core aspects of open-ended evolution. However, the manner in which these phenomena relate remains an area of great theoretical interest. It is yet to be firmly established to what extent these phenomena are coupled and by what means they interact. In this work, we track the co-evolution of novelty, complexity, and adaptation in a case study from a simulation system designed to study the evolution of digital multicellularity. In this case study, we describe ten qualitatively distinct multicellular morphologies, several of which exhibit asymmetrical growth and distinct life stages. We contextualize the evolutionary history of these morphologies with measurements of complexity and adaptation. Our case study suggests a loose, sometimes divergent, relationship can exist among novelty, complexity, and adaptation.

Matthew Andres Moreno, Santiago Rodriguez Papa and Charles Ofria. 2021. Case Study of Novelty, Complexity, and Adaptation in a Multicellular System. OEE4: The Fourth Workshop on Open-Ended Evolution. https://doi.org/10.48550/arXiv.2405.07241

• extended preprint via arXiv ☠️
• supplement via Open Science Framework ❋
• data via Open Science Framework ❋
• source code via GitHub
• web demo
• presentation via YouTube
• slides
• LaTeX source via GitHub

Developing software to effectively take advantage of growth in parallel and distributed processing capacity poses significant challenges. Traditional programming techniques allow a user to assume that execution, message passing, and memory are always kept synchronized. However, maintaining this consistency becomes increasingly costly at scale. One proposed strategy is “best-effort computing”, which relaxes synchronization and hardware reliability requirements, accepting nondeterminism in exchange for efficiency. Although many programming languages and frameworks aim to facilitate software development for high performance applications, existing tools do not directly provide a prepackaged best-effort interface. The Conduit C++ Library aims to provide such an interface for convenient implementation of software that uses best-effort inter-thread and inter-process communication. Here, we describe the motivation, objectives, design, and implementation of the library. Benchmarks on a communication-intensive graph coloring problem and a compute-intensive digital evolution simulation show that Conduit’s best-effort model can improve scaling efficiency and solution quality, particularly in a distributed, multi-node context.

Matthew Andres Moreno, Santiago {Rodriguez Papa}, and Charles Ofria. 2021. Conduit: a C++ library for best-effort high performance computing. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ‘21). Association for Computing Machinery, New York, NY, USA, 1795–1800. https://doi.org/10.1145/3449726.3463205

• data via Open Science Framework ❋
• interactive data notebooks via MyBinder ❋
• graph coloring benchmark source code via GitHub
• digital evolution benchmark source code via GitHub
• slides via Google Slides
• LaTeX source via GitHub

The emergence of new replicating entities from the union of existing entities represent some of the most profound events in natural evolutionary history. Facilitating such evolutionary transitions in individuality is essential to the derivation of the most complex forms of life. As such, understanding these transitions is critical for building artificial systems capable of open-ended evolution. Alas, these transitions are challenging to induce or detect, even with computational organisms. Here, we introduce the DISHTINY (DIStributed Hierarchical Transitions in IndividualitY) platform, which provides simple cell-like organisms with the ability and incentive to unite into new individuals in a manner that can continue to scale to subsequent transitions. The system is designed to encourage these transitions so that they can be studied: organisms that coordinate spatiotemporally can maximize the rate of resource harvest, which is closely linked to their reproductive ability. We demonstrate the hierarchical emergence of multiple levels of individuality among simple cell-like organisms that evolve parameters for manually-designed strategies. During evolution, we observe reproductive division of labor and close cooperation between cells, including resource-sharing, aggregation of resource endowments for propagules, and emergence of an apoptosis response to somatic mutation. While a few replicate populations evolved selfish behaviors, many evolved to direct their resources toward low-level groups (behaving like multi-cellular individuals), and many others evolved to direct their resources toward high-level groups (acting as larger-scale multi-cellular individuals). Finally, we demonstrated that genotypes that encode higher-level individuality consistently outcompete those that encode lower-level individuality.

Matthew Andres Moreno and Charles Ofria. 2018. Understanding Fraternal Transitions in Individuality. OEE3: The Third Workshop on Open-Ended Evolution.

• data via Open Science Framework ❋
• how-to tutorials via Open Science Framework ❋
• source code via GitHub
• web demo
• presentation via YouTube
• slides
• LaTeX source via GitHub

Emerging ML/AI hardware accelerators, like the 850,000 processor Cerebras Wafer-Scale Engine (WSE), hold great promise to scale up the capabilities of evolutionary computation. However, challenges remain in maintaining visibility into underlying evolutionary processes while efficiently utilizing these platforms’ large processor counts. Here, we focus on the problem of extracting phylogenetic history. We present a tracking-enabled asynchronous island-based genetic algorithm (GA) framework for WSE hardware. Emulated and on-hardware GA benchmarks with a simple tracking-enabled agent model clock upwards of 1 million generations per minute for population sizes reaching 16 million. We validate phylogenetic reconstructions from these trials and demonstrate their suitability for inference of underlying evolutionary conditions. In particular, we demonstrate extraction of clear phylometric signals that differentiate adaptive dynamics. Kernel code implementing the island-model GA supports drop-in customization to support any fixed-length genome content and fitness criteria, benefiting further explorations within the evolutionary biology and evolutionary computation communities.

Matthew Andres Moreno, Connor Yang, Emily Dolson, and Luis Zaman. 2024. Trackable Agent-Based Evolution Models at Wafer Scale. In SC24 Research Poster and ACM Student Research Competition Poster Archive. https://sc24.supercomputing.org/proceedings/poster/poster_pages/post166.html

• LaTeX source via GitHub
• primary publication
• poster

Emerging ML/AI hardware accelerators, like the 850,000 processor Cerebras Wafer-Scale Engine (WSE), hold great promise to scale up the capabilities of evolutionary computation. However, challenges remain in maintaining visibility into underlying evolutionary processes while efficiently utilizing these platforms’ large processor counts. Here, we focus on the problem of extracting phylogenetic information from digital evolution on the WSE platform. We present a tracking-enabled asynchronous island-based genetic algorithm (GA) framework for WSE 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. This pace enables quadrillions of evaluations a day. We validate phylogenetic reconstructions from these trials and demonstrate their suitability for inference of underlying evolutionary conditions. In particular, we demonstrate extraction of clear phylometric signals that differentiate wafer-scale runs with adaptive dynamics enabled versus disabled. Together, these benchmark and validation trials reflect strong potential for highly scalable evolutionary computation that is both efficient and observable. Kernel code implementing the island-model GA supports drop-in customization to support any fixed-length genome content and fitness criteria, allowing it to be leveraged to advance research interests across the community.

Matthew Andres Moreno, Connor Yang, Emily Dolson, and Luis Zaman. 2024. Trackable Island-model Genetic Algorithms at Wafer Scale. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO ‘24 Companion). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3638530.3664090

• LaTeX source via GitHub
• primary publication

Complexity is a signature quality of interest in artificial life systems. Alongside other dimensions of assessment, it is common to quantify genome sites that contribute to fitness as a complexity measure. However, limitations to the sensitivity of fitness assays in models with implicit replication criteria involving rich biotic interactions introduce the possibility of difficult-to-detect “cryptic” adaptive sites, which contribute small fitness effects below the threshold of individual detectability or involve epistatic redundancies. Here, we propose three knockout-based assay procedures designed to quantify cryptic adaptive sites within digital genomes. We report initial tests of these methods on a simple genome model with explicitly configured site fitness effects. In these limited tests, estimation results reflect ground truth cryptic sequence complexities well. Presented work provides initial steps toward development of new methods and software tools that improve the resolution, rigor, and tractability of complexity analyses across alife systems, particularly those requiring expensive in situ assessments of organism fitness.

Moreno, M. A. (2024). Methods to Estimate Cryptic Sequence Complexity. In The 2024 Conference on Artificial Life. MIT Press. https://doi.org/10.1162/isal_a_00776

• manuscript source and software via GitHub
• data notebooks via GitHub
• conference slides via Google Slides

This Hot-off-the-Press paper summarizes our recently published work, “Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity.” This work appeared in Genetic Programming and Evolvable Machines. Genetic programming systems commonly use tag matching to decide interactions between system components. However, the implications of criteria used to determine affinity between tags with respect evolutionary dynamics have not been directly studied. We investigate differences between tag-matching criteria with respect to geometric constraint and variation generated under mutation. In experiments, we find that tag-matching criteria can influence the rate of adaptive evolution and the quality of evolved solutions. Better understanding of the geometric, variational, and evolutionary properties of tag-matching criteria will facilitate more effective incorporation of tag matching into genetic programming systems. By showing that tag-matching criteria influence connectivity patterns and evolutionary dynamics, our findings also raise fundamental questions about the properties of tag-matching systems in nature.

Matthew Andres Moreno, Alexander Lalejini, and Charles Ofria. 2023. Tag Affinity Criteria Influence Adaptive Evolution. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO ‘23 Companion). Association for Computing Machinery, New York, NY, USA, 35–36. https://doi.org/10.1145/3583133.3595834

• presentation via YouTube
• slides via Google Slides
• LaTeX source via GitHub
• primary publication

This Hot-off-the-Press paper summarizes our recently published work, “Tag-based regulation of modules in genetic programming improves context-dependent problem solving,” published in Genetic Programming and Evolvable Machines. We introduce and experimentally demonstrate tag-based genetic regulation, a genetic programming (GP) technique that allows programs to dynamically adjust which code modules to express. Tags are evolvable labels that provide a flexible naming scheme for referencing code modules. Tag-based regulation extends tag-based naming schemes to allow programs to “promote” and “repress” code modules to alter module execution patterns. We find that tag-based regulation improves problem-solving success on problems where programs must adjust how they respond to current inputs based on prior inputs; indeed, some of these problems could not be solved until regulation was added. We also identify scenarios where the correct response to an input does not change over time, rendering tag-based regulation an unnecessary functionality that can sometimes impede evolution. Broadly, tag-based regulation adds to our repertoire of techniques for evolving more dynamic computer programs and can easily be incorporated into existing tag-enabled GP systems.

Alexander Lalejini, Matthew Andres Moreno, and Charles Ofria. 2022. Tag-based Module Regulation for Genetic Programming. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ‘22). Association for Computing Machinery, New York, NY, USA, 25–26. https://doi.org/10.1145/3520304.3534060

• primary publication

Phylogenetic analyses can also enable insight into evolutionary and ecological dynamics such as selection pressure and frequency dependent selection in digital evolution systems. Traditionally digital evolution systems have recorded data for phylogenetic analyses through perfect tracking where each birth event is recorded in a centralized data structures. 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 infer phylogenies via heritable genetic annotations rather than directly track them. We introduce a “hereditary stratigraphy” algorithm that enables efficient, accurate phylogenetic reconstruction with tunable, explicit trade-offs between annotation memory footprint and reconstruction accuracy. 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 a 64-bit annotation.

Matthew Andres Moreno, Emily Dolson, and Charles Ofria. 2022. Hereditary stratigraphy: genome annotations to enable phylogenetic inference over distributed populations. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ‘22). Association for Computing Machinery, New York, NY, USA, 65–66. https://doi.org/10.1145/3520304.3533937

• data via Open Science Framework ❋
• source code via GitHub
• hstrat library code via GitHub
• web demo 1
• web demo 2
• presentation via YouTube
• poster via Google Slides
• slides via Google Slides
• LaTeX source via GitHub

Developing software to effectively take advantage of growth in parallel and distributed processing capacity poses significant challenges. Best-effort computing models, which relax synchronization requirements, have been proposed as a strategy to overcome challenges harness high performance computing at extreme scale. Although many programming languages and frameworks aim to facilitate software development for high performance applications, existing prevalent tools do not expose an explicit best-effort interface. The Conduit C++ Library aims to provide a convenient interface for best-effort inter-thread and inter-process communication. Here, we describe the motivation, objectives, design, and implementation of the library.

Matthew Andres Moreno, Santiago Rodriguez Papa and Charles Ofria. 2021. Conduit: A C++ Library for Best-Effort High Performance Computing. MSPDS 2020: The 6th International Workshop on Modeling and Simulation of and by Parallel and Distributed Systems.

• poster
• LaTeX source via GitHub

Due to ongoing accrual over long durations, a defining characteristic of real-world data streams is the requirement for rolling, often real-time, mechanisms to coarsen or summarize stream history. One common data structure for this purpose is the ring buffer, which maintains a running downsample comprising most recent stream data. In some downsampling scenarios, however, it can instead be necessary to maintain data items spanning the entirety of elapsed stream history. Fortunately, approaches generalizing the ring buffer mechanism have been devised to support alternate downsample compositions, while maintaining the ring buffer’s update efficiency and optimal use of memory capacity. The Downstream library implements algorithms supporting three such downsampling generalizations: (1) “steady,” which curates data evenly spaced across the stream history; (2) “stretched,” which prioritizes older data; and (3) “tilted,” which prioritizes recent data. To enable a broad spectrum of applications ranging from embedded devices to high-performance computing nodes and AI/ML hardware accelerators, Downstream supports multiple programming languages, including C++, Rust, Python, Zig, and the Cerebras Software Language. For seamless interoperation, the library incorporates distribution through multiple packaging frameworks, extensive cross-implementation testing, and cross-implementation documentation.

Yang C., Wagner J., Dolson E., Zaman L., & Moreno M. A. (2025). Downstream: efficient cross-platform algorithms for fixed-capacity stream downsampling. arXiv preprint arXiv:2506.12975. https://doi.org/10.48550/arXiv.2506.12975

• manuscript source via GitHub
• software repository via GitHub
• software package via PyPI
• software package via crates.io
• notebooks via GitHub
• data via Open Science Framework ❋

Operations over data streams typically hinge on efficient mechanisms to aggregate or summarize history on a rolling basis. For high-volume data steams, it is critical to manage state in a manner that is fast and memory efficient — particularly in resource-constrained or real-time contexts. Here, we address the problem of extracting a fixed-capacity, rolling subsample from a data stream. Specifically, we explore “data stream curation” strategies to fulfill requirements on the composition of sample time points retained. Our “DStream” suite of algorithms targets three temporal coverage criteria: (1) steady coverage, where retained samples should spread evenly across elapsed data stream history; (2) stretched coverage, where early data items should be proportionally favored; and (3) tilted coverage, where recent data items should be proportionally favored. For each algorithm, we prove worst-case bounds on rolling coverage quality. We focus on the more practical, application-driven case of maximizing coverage quality given a fixed memory capacity. As a core simplifying assumption, we restrict algorithm design to a single update operation: writing from the data stream to a calculated buffer site — with data never being read back, no metadata stored (e.g., sample timestamps), and data eviction occurring only implicitly via overwrite. Drawing only on primitive, low-level operations and ensuring full, overhead-free use of available memory, this “DStream” framework ideally suits domains that are resource-constrained, performance-critical, and fine-grained (e.g., individual data items as small as single bits or bytes). The proposed approach supports O(1) data ingestion via concise bit-level operations. To further practical applications, we provide plug-and-play open-source implementations targeting both scripted and compiled application domains.

Moreno M. A., Zaman L., & Dolson E. (2024). Structured Downsampling for Fast, Memory-efficient Curation of Online Data Streams. arXiv preprint arXiv:2409.06199. https://doi.org/10.48550/arXiv.2409.06199

• manuscript source via GitHub
• software repository via GitHub
• software package via PyPI

Computer simulations are an important tool for studying the mechanics of biological evolution. In particular, in silico work with agent-based models provides an opportunity to collect high-quality records of ancestry relationships among simulated agents. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Existing work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, direct tracking can be inefficient for large-scale, many-processor evolutionary simulations. An alternate approach to extracting phylogenetic information from simulation that scales more favorably is post hoc estimation, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recently introduced “hereditary stratigraphy” algorithms provide means for efficient inference of phylogenetic history from non-coding annotations on simulated organisms’ genomes. A number of options exist in configuring hereditary stratigraphy methodology, but no work has yet tested how they impact reconstruction quality. To address this question, we surveyed reconstruction accuracy under alternate configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. We synthesize results from these experiments to suggest a prescriptive system of best practices for work with hereditary stratigraphy, ultimately guiding researchers in choosing appropriate instrumentation for large-scale simulation studies.

Moreno, M. A., Ranjan, A., Dolson, E., & Zaman, L. (2024). A Guide to Tracking Phylogenies in Parallel and Distributed Agent-based Evolution Models. arXiv preprint arXiv:2405.10183. https://doi.org/10.48550/arXiv.2405.10183

• manuscript source via GitHub
• code and notebooks via GitHub
• data via Open Science Framework ❋

In silico evolution instantiates the processes of heredity, variation, and differential reproductive success (the three “ingredients” for evolution by natural selection) within digital populations of computational agents. Consequently, these populations undergo evolution, and can be used as virtual model systems for studying evolutionary dynamics. This experimental paradigm — used across biological modeling, artificial life, and evolutionary computation — complements research done using in vitro and in vivo systems by enabling experiments that would be impossible in the lab or field. One key benefit is complete, exact observability. For example, it is possible to perfectly record all parent-child relationships across simulation history, yielding complete phylogenies (ancestry trees). This information reveals when traits were gained or lost, and also facilitates inference of underlying evolutionary dynamics.

The Phylotrack project provides libraries for tracking and analyzing phylogenies in in silico evolution. The project is composed of 1) Phylotracklib: a header-only C++ library, developed under the umbrella of the Empirical project, and 2) Phylotrackpy: a Python wrapper around Phylotracklib, created with Pybind11. Both components supply a public-facing API to attach phylogenetic tracking to digital evolution systems, as well as a stand-alone interface for measuring a variety of popular phylogenetic topology metrics. Underlying design and C++ implementation prioritizes efficiency, allowing for fast generational turnover for agent populations numbering in the tens of thousands. Several explicit features (e.g., phylogeny pruning and abstraction, etc.) are provided for reducing the memory footprint of phylogenetic information.

Dolson, E., Rodriguez-Papa, S., & Moreno, M. A. (2024). Phylotrack: C++ and Python libraries for in silico phylogenetic tracking. arXiv preprint arXiv:2405.09389. https://doi.org/10.48550/arXiv.2405.09389

• manuscript source via GitHub
• software repository via GitHub
• software package via PyPI
• software documentation via ReadTheDocs 📖
• data via Open Science Framework ❋

Since the advent of modern bioinformatics, the challenging, multifaceted problem of reconstructing phylogenetic history from biological sequences has hatched perennial statistical and algorithmic innovation. Studies of the phylogenetic dynamics of digital, agent-based evolutionary models motivate a peculiar converse question: how to best engineer tracking to facilitate fast, accurate, and memory-efficient lineage reconstructions? Here, we formally describe procedures for phylogenetic analysis in both serial and distributed computing scenarios. With respect to the former, we demonstrate reference-counting-based pruning of extinct lineages. For the latter, we introduce a trie-based phylogenetic reconstruction approach for “hereditary stratigraphy” genome annotations. This process allows phylogenetic relationships between genomes to be inferred by comparing their similarities, akin to reconstruction of natural history from biological DNA sequences. Phylogenetic analysis capabilities significantly advance distributed agent-based simulations as a tool for evolutionary research, and also benefit application-oriented evolutionary computing. Such tracing could extend also to other digital artifacts that proliferate through replication, like digital media and computer viruses.

Moreno, M. A., Rodriguez Papa, S., & Dolson, E. (2024). Analysis of Phylogeny Tracking Algorithms for Serial and Multiprocess Applications. arXiv preprint arXiv:2403.00246 https://doi.org/10.48550/arXiv.2403.00246

• manuscript source via GitHub

Data stream algorithms tackle operations on high-volume sequences of read-once data items. Data stream scenarios include inherently real-time systems like sensor networks and financial markets. They also arise in purely-computational scenarios like ordered traversal of big data or long-running iterative simulations. In this work, we develop methods to maintain running archives of stream data that are temporally representative, a task we call “stream curation.” Our approach contributes to rich existing literature on data stream binning, which we extend by providing stateless (i.e., non-iterative) curation schemes that enable key optimizations to trim archive storage overhead and streamline processing of incoming observations. We also broaden support to cover new trade-offs between curated archive size and temporal coverage. We present a suite of five stream curation algorithms that span O(n), O(logn), and O(1) orders of growth for retained data items. Within each order of growth, algorithms are provided to maintain even coverage across history or bias coverage toward more recent time points. More broadly, memory-efficient stream curation can boost the data stream mining capabilities of low-grade hardware in roles such as sensor nodes and data logging devices.

Moreno, M. A., Rodriguez Papa, S., & Dolson, E. (2024). Algorithms for Efficient, Compact Online Data Stream Curation. arXiv preprint arXiv:2403.00266. https://doi.org/10.48550/arXiv.2403.00266

• manuscript source via GitHub

Here, we test the performance and scalability of fully-asynchronous, best-effort communication on existing, commercially-available HPC hardware.

A first set of experiments tested whether best-effort communication strategies can benefit performance compared to the traditional perfect communication model. At high CPU counts, best-effort communication improved both the number of computational steps executed per unit time and the solution quality achieved within a fixed-duration run window.

Under the best-effort model, characterizing the distribution of quality of service across processing components and over time is critical to understanding the actual computation being performed. Additionally, a complete picture of scalability under the best-effort model requires analysis of how such quality of service fares at scale. To answer these questions, we designed and measured a suite of quality of service metrics: simulation update period, message latency, message delivery failure rate, and message delivery coagulation. Under a lower communication-intensivity benchmark parameterization, we found that median values for all quality of service metrics were stable when scaling from 64 to 256 process. Under maximal communication intensivity, we found only minor – and, in most cases, nil – degradation in median quality of service.

In an additional set of experiments, we tested the effect of an apparently faulty compute node on performance and quality of service. Despite extreme quality of service degradation among that node and its clique, median performance and quality of service remained stable.

Moreno, M. A., & Ofria, C. (2022). Best-Effort Communication Improves Performance and Scales Robustly on Conventional Hardware. arXiv preprint arXiv:2211.10897.

• data via Open Science Framework ❋
• source code via GitHub
• manuscript source via GitHub

Developing software to effectively take advantage of growth in parallel and distributed processing capacity poses significant challenges. Traditional programming techniques allow a user to assume that execution, message passing, and memory are always kept synchronized. However, maintaining this consistency becomes increasingly costly at scale. One proposed strategy is “best-effort computing”, which relaxes synchronization and hardware reliability requirements, accepting nondeterminism in exchange for efficiency. Although many programming languages and frameworks aim to facilitate software development for high performance applications, existing tools do not directly provide a prepackaged best-effort interface. The Conduit C++ Library aims to provide such an interface for convenient implementation of software that uses best-effort inter-thread and inter-process communication. Here, we describe the motivation, objectives, design, and implementation of the library. Benchmarks on a communication-intensive graph coloring problem and a compute-intensive digital evolution simulation show that Conduit’s best-effort model can improve scaling efficiency and solution quality, particularly in a distributed, multi-node context.

Moreno, M. A., Rodriguez Papa, S., & Ofria, C. (2021). SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications. arXiv preprint arXiv:2108.00382.

• data via Open Science Framework ❋
• source code via GitHub
• slides via Google Slides

Evolutionary transitions occur when previously-independent replicating entities unite to form more complex individuals. Such major transitions in individuality have profoundly shaped complexity, novelty, and adaptation over the course of natural history. Regard for their causes and consequences drives many fundamental questions in biology. Likewise, evolutionary transitions have been highlighted as a hallmark of true open-ended evolution in artificial life. As such, experiments with digital multicellularity promise to help realize computational systems with properties that more closely resemble those of biological systems, ultimately providing insights about the origins of complex life in the natural world and contributing to bio-inspired distributed algorithm design.

Major challenges exist, however, in applying high-performance computing to the dynamic, large-scale digital artificial life simulations required for such work. This dissertation presents two new tools that facilitate such simulations at scale: the Conduit library for best-effort communication and the hstrat (“hereditary stratigraphy”) library, which debuts novel decentralized algorithms to estimate phylogenetic distance between evolving agents.

Most current high-performance computing work emphasizes logical determinism: extra effort is expended to guarantee reliable communication between processing elements. When necessary, computation halts in order to await expected messages. Determinism does enable hardware-independent results and perfect reproducibility, however adopting a best-effort communication model can substantially reduce synchronization overhead and allow dynamic (albeit, potentially lossy) scaling of communication load to fully utilize available resources. We present a set of experiments that test the best-effort communication model implemented by the Conduit library on commercially available high-performance computing hardware. We find that best-effort communication enables significantly better computational performance under high thread and process counts and can achieve significantly better solution quality within a fixed time constraint.

In a similar vein, phylogenetic analysis in digital evolution work has traditionally used a perfect tracking model where each birth event is recorded in a centralized data structure. This approach, however, is difficult scale robustly and efficiently to distributed computing environments where agents may migrate between a dynamic set of disjoint processing elements. To provide for phylogenetic analyses in these environments, we propose an approach to infer phylogenies via heritable genetic annotations. We introduce hereditary stratigraphy, an algorithm that enables tunable trade-offs between annotation memory footprint and accuracy of phylogenetic inference. Simulating inference over known lineages, we recover up to 85% of the information contained in the true phylogeny using only a 64-bit annotation.

We harness these tools in DISHTINY, a distributed digital evolution system designed to study digital organisms as they undergo major evolutionary transitions in individuality. This system allows digital cells to form and replicate kin groups by selectively adjoining or expelling daughter cells. The capability to recognize kin-group membership enables preferential communication and cooperation between cells. We report group-level traits characteristic of fraternal transitions, including reproductive division of labor, resource sharing within kin groups, resource investment in offspring groups, asymmetrical behaviors mediated by messaging, morphological patterning, and adaptive apoptosis. In one detailed case study, we track the co-evolution of novelty, complexity, and adaptation over the evolutionary history of an experiment. We characterize ten qualitatively distinct multicellular morphologies, several of which exhibit asymmetrical growth and distinct life stages. Our case study suggests a loose relationship can exist among novelty, complexity, and adaptation.

The constructive potential inherent in major evolutionary transitions holds great promise for progress toward replicating the capability and robustness of natural organisms. Coupled with shrewd software engineering and innovative model design informed by evolutionary theory, contemporary hardware systems could plausibly already suffice to realize paradigm-shifting advances in open-ended evolution and, ultimately, scientific understanding of major transitions themselves. This work establishes important new tools and methodologies to support continuing progress in this direction.

Moreno, Matthew Andres. 2022. “Engineering Scalable Digital Models to Study Major Transitions in Evolution.” Order No. 29999702, Michigan State University. http://ezproxy.msu.edu/login?url=https://www.proquest.com/dissertations-theses/engineering-scalable-digital-models-study-major/docview/2754890561/se-2.

• presentation via YouTube
• flyer
• LaTeX source via GitHub
• slides via Google Slides

Biological organisms are thought to possess traits that facilitate evolution. The term evolvability was coined to describe this type of adaptation. The question of evolvability has special practical relevance to computer science researchers engaged in longstanding efforts to harness evolution as an algorithm for automated design. It is hoped that a more nuanced understanding of evolvability inspired by biological evolution will translate to more powerful digital evolution techniques. To this end, the relationship between evolvability and environmental influence on the phenotype was investigated using digital experiments performed on a genetic regulatory model. The phenotypic response of champion individuals evolved under regimes of direct plasticity, and indirect plasticity was assessed. The model predicts that direct plasticity and indirect plasticity decrease and increase the frequency of silent mutations, respectively.

• presentation
• presentation flyer
• poster

Biological organisms exhibit spectacular adaptation to their environments. However, another marvel of biology lurks behind the adaptive traits that organisms exhibit over the course of their lifespans: it is hypothesized that biological organisms also exhibit adaptation to the evolutionary process itself. That is, biological organisms are thought to possess traits that facilitate evolution. The term evolvability was coined to describe this type of adaptation. The question of evolvability has special practical relevance to computer science researchers engaged in longstanding efforts to harness evolution as an algorithm for automated design. It is hoped that a more nuanced understanding of biological evolution will translate to more powerful digital evolution techniques. This thesis will present a theoretical overview of evolvability, illustrated with examples from biology and evolutionary computing, and discuss computational experiments probing the relationship between environmental influence on the phenotype and evolvability.

Moreno, Matthew Andres, “Evolvability: What Is It and How Do We Get It?” (2017). Honors Program Theses. 22. https://soundideas.pugetsound.edu/honors_program_theses/22

• slides
• flyer
• LaTeX source via GitHub

We present a model of traffic in the greater Seattle area to understand how an increasing frequency of self-driving cars will change traffic dynamics in the area. We apply a two-component micro/macro traffic simulation to data for portions of Interstates 5, 90, 405, and State Route 520 to consider the impact of autonomous vehicles on regional traffic flow. We consider 0%, 10%, 50%, and 90% autonomous traffic.

Our micro model is designed to make predictions about the impact of self-driving vehicles on fundamental traffic dynamics and employs a cellular automata approach, inspired by the work of Nagel and Schrekenberg, to model interactions between a number of independent vehicles on a road. In this simulation, vehicles exhibit simple following behavior and experience occasional random deceleration events. We introduce a distinction between self-driving and human-driven cars, where autonomous vehicles exhibit more uniform cruising speed compared to human drivers and can follow safely at a much closer distance compared to human drivers.

Using this micro-level simulation, we predict a relation between traffic speed and traffic density for traffic with a varying composition of autonomous vehicles. Our macro model employs a system of ordinary differential equations to investigate the flow of traffic between segments of road in the region of study. We assess the impact of self-driving traffic composition on performance of the regional highway network at peak and average traffic loads, measuring trip times along each major highway and between a representative set of regional destinations. The travel time predictions of the macro model are compared to archived travel time data from the the Washington State Department of Transportation (WSDOT).

These models, in conjunction, facilitate insightful study of how different percentages of self-driving cars on the motorways change traffic flow under heavy and light traffic conditions. The quantitative accuracy of our macro model is observed to decline significantly with increasing traffic loads. Nevertheless, the results of our study demonstrate clear qualitative trends that inform our recommendations. Although our macro model does not make quantitatively accurate predictions, we observe a trend indicating that at high traffic densities, traffic delays decrease with increasing percentages of self-driving cars on the road.

Analysis of our micro model reveals that assigning traffic lanes for the exclusive use of autonomous vehicles can be a boon to traffic flow efficiency. When the concentration of self-driving cars rises to above 5%, our micro model predicts that it becomes advantageous to implement at least one “self-driving-car only” lane in roads with 3 or more lanes. Under some circumstances, this strategy has the potential to result in reduced travel delays for human-driven and autonomously controlled vehicles alike.

• press release
• Seattle Times article
• presentation
• presentation flyer

Session participants will walk through an interactive, zero-code tutorial demonstrating how to create and manage a public-facing class blog using the Jekyll site framework and GitHub pages. We will also discuss milestone-based project structure to guide students to successful project completion and authorial strategies to create engaging scholarly web-based content.

After this session, participants will be equipped to:

1. create a Jekyll-based class blog on GitHub pages,
2. guide student authorship of engaging scholarly blog posts that incorporate Markdown-based styling and multimedia elements,
3. structure a milestone-based deadline schedule to help students stay on track for successful preparation of a high-quality written work,
4. facilitate student peer review using GitHub pull requests, and
5. streamline student submission of draft milestones and final piece for publication using pull request status labels.

The majority of the session will consist of a guided tutorial experience in which participants will create mock blog posts and engage in a mock peer review process. These activities will be fully accessible to participants on any platform, including mobile devices, through browser-based interfaces. No coding will be required.

• example blog via GitHub Pages
• example blog repository [via GitHub ](
• slides via Google Slides
• Teaching and Technology Collaborative
• call for proposals

• BI/O website
• BI/O website repository
• UMich EEB Press Release

Phylogenies (i.e., ancestry trees) group extant organisms by ancestral relatedness to render the history of hierarchical lineage branching events within an evolving system. These relationships reveal the evolutionary trajectories of populations through a genotypic or phenotypic space. As such, phylogenies open a direct window through which to observe ecology, differential selection, genetic potentiation, emergence of complex traits, and other evolutionary dynamics in artificial life (ALife) systems. In evolutionary biology, phylogenies are often estimated from the fossil record, phenotypic traits, and extant genetic information. Although substantially limited in precision, such phylogenies have profoundly advanced our understanding of the evolution of life on Earth. In digital systems, we often have the ability to create perfect (or near perfect) phylogenies that reveal the step-by-step process by which evolution unfolds. However, phylogeny tracking and phylogeny-based analyses are not yet commonplace in ALife. Fortunately, a number of software tools have recently become available to facilitate such analyses, such as Phylotrackpy, DEAP, Empirical, MABE, and hstrat.

Biologists have developed many sophisticated and powerful phylogeny-based analysis techniques. For example, existing work uses properties of tree topology to infer characteristics of the evolutionary processes acting on a population. With an understanding of the differences between biology and artificial life, these approaches can be imported into ALife systems. For example, phylodiversity metrics can be used to detect diversity-maintaining ecological interactions and ongoing generation of significant evolutionary innovations.

This tutorial will provide an introduction to phylogenies, how to record them in digital systems, and use cases for phylogenetic analyses in an artificial life context. We will open with a quick discussion of prior research enabled by and based on phylogenies in digital evolution systems. We will then survey existing phylogeny software tools and lead interactive tutorials on tracking phylogenies in both traditional and distributed computing environments. Next, we will demonstrate measurements and data visualizations that phylogenetic data enables, including Muller plots, phylogenetic topology metrics, and annotated phylogeny visualizations. Lastly, we will discuss open questions and future directions related to phylogenies in artificial life.

• web page
• slides via Google Slides

• slides 1 via Google Slides
• slides 2 via Google Slides
• lecture 1 via YouTube
• lecture 2 via YouTube

Class blogs have grown into a core tool of the educational experiences, like the CSE 491 Advanced C++ Seminar and this summer’s WAVES Workshop, I’ve had the pleasure of facilitating. I typically have students contribute to the blog as part of their own learning experience.

I love this format because it helps,

• develop students’ professional communication skills,
• provide students a sense of accomplishment via a tangible, rewarding deliverable,
• showcase students’ work to the general public,
• showcase students’ work to potential employers,
• showcase students’ work to mentors’ evaluators,
• more effectively capitalize on students’ work after they leave the lab group or classroom, and
• sausage factory more useful information out into the ether that someday somebody will be very happy to have Googled upon.

This writeup provides guidance targeted to students writing entries for these class blogs (hi! ). It should also contain a few actionable nuggets for other authors writing professional blog posts with Jekyll, though! I hope that other instructors, in particular, may find this a useful resource for bringing similar models into their classroom.

Matthew Andres Moreno. (2024). mmore500/pecking. Zenodo. https://doi.org/10.5281/zenodo.10701185

• documentation via GitHub
• source archive via Zenodo z

Matthew Andres Moreno. (2024). mmore500/colorclade. Zenodo. https://doi.org/10.5281/zenodo.10802404

• documentation via GitHub
• source archive via Zenodo z

Matthew Andres Moreno. (2023). mmore500/outset. Zenodo. https://doi.org/10.5281/zenodo.10426106

• documentation via GitHub Pages
• source archive via Zenodo z
• A Killer Fix for Scrunched Axes, Step-by-step, article via towards data science
• A Comprehensive Guide to Inset Axes in Matplotlib, article via towards data science
• Let Your Data Breathe: Tips, tricks, & tools to level up your FacetGrid game, article via level up coding

Dolson, E., Rodriguez-Papa, S., & Moreno, M. A. (2024). Phylotrack: C++ and Python libraries for in silico phylogenetic tracking. arXiv preprint arXiv:2405.09389. https://doi.org/10.48550/arXiv.2405.09389

• documentation via ReadTheDocs 📖
• source archive via Zenodo z

Moreno M.A., Dolson, E., & Ofria, C. (2022). hstrat: a Python Package for phylogenetic inference on distributed digital evolution populations. Journal of Open Source Software, 7(80), 4866, https://doi.org/10.21105/joss.04866

• documentation via ReadTheDocs 📖

Matthew Andres Moreno, Santiago Rodriguez Papa. (2024). mmore500/alifedata-phyloinformatics-convert. Zenodo. https://doi.org/10.5281/zenodo.10701178

• documentation via ReadTheDocs 📖
• source archive via Zenodo z

Moreno, M. A., {Rodriguez Papa}, S., & Ofria, C. (2021). SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications. arXiv preprint arXiv:2108.00382.

• documentation via ReadTheDocs 📖

Matthew Andres Moreno, Santiago Rodriguez Papa, and Charles Ofria. 2021. Conduit: a C++ library for best-effort high performance computing. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ‘21). Association for Computing Machinery, New York, NY, USA, 1795–1800. https://doi.org/10.1145/3449726.3463205

• documentation via ReadTheDocs 📖
• source archive via Zenodo z

• documentation via ReadTheDocs 📖
• web interface via GitHub

Ofria, C., Moreno, M. A., Dolson, E., Lalejini, A., Rodriguez Papa, S., Fenton, J., Perry, K., Jorgensen, S., , H., , G., Baldwin Edwards, O., Stredwick, J., , C., , T., Vostinar, A., Moreno, R., Schossau, J., Zaman, L., & , D. (2020). Empirical: C++ library for efficient, reliable, and accessible scientific software (Version 0.0.4) [Computer software]. https://doi.org/10.5281/zenodo.4141943

• documentation via ReadTheDocs 📖
• poster via Google Slides

• data via Open Science Framework ❋

• announcement via Twitter

Matthew Andres Moreno (2024). mmore500/qspool. Zenodo. https://doi.org/10.5281/zenodo.10864602

• documentation via GitHub
• source archive via Zenodo z

Matthew Andres Moreno. (2024). mmore500/joinem. Zenodo. https://doi.org/10.5281/zenodo.10701182

• documentation via GitHub
• source archive via Zenodo z

• documentation via ReadTheDocs 📖

• documentation via ReadTheDocs 📖

Matthew Andres Moreno. (2023). mmore500/teeplot. Zenodo. https://doi.org/10.5281/zenodo.10440670

• documentation
• source archive via Zenodo z
• Tips and Tricks to Organize Jupyter Notebook Visualizations, article via towards data science

• documentation via ReadTheDocs 📖

Computer simulations are an important tool for studying the mechanics of biological evolution. In particular, in silico work with agent-based models provides an opportunity to collect high-quality records of ancestry relationships among simulated agents. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Existing work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, direct tracking can be inefficient for large-scale, many-processor evolutionary simulations. An alternate approach to extracting phylogenetic information from simulation that scales more favorably is post hoc estimation, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recently introduced “hereditary stratigraphy” algorithms provide means for efficient inference of phylogenetic history from non-coding annotations on simulated organisms’ genomes. A number of options exist in configuring hereditary stratigraphy methodology, but no work has yet tested how they impact reconstruction quality. To address this question, we surveyed reconstruction accuracy under alternate configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. We synthesize results from these experiments to suggest a prescriptive system of best practices for work with hereditary stratigraphy, ultimately guiding researchers in choosing appropriate instrumentation for large-scale simulation studies.

Moreno, M. A., Ranjan, A., Dolson, E., & Zaman, L. (2024). A Guide to Tracking Phylogenies in Parallel and Distributed Agent-based Evolution Models. arXiv preprint arXiv:2405.10183. https://doi.org/10.48550/arXiv.2405.10183

• manuscript source via GitHub
• code and notebooks via GitHub
• data via Open Science Framework ❋