Research

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

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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

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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

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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

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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.

Moreno, M. A., Rodriguez-Papa, S., & Dolson, E. (2024). Ecology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure. arXiv preprint arXiv:2405.07245.

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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

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

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

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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

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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

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• hstrat library code via GitHub
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• presentation via YouTube
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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

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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

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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

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• hstrat library code via GitHub
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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

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• 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

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 📖

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/10.48550/arXiv.2404.10854

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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

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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.

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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

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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.

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• source code via GitHub
• web demo
• presentation via YouTube
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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

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

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.

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• 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.

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

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• graph coloring benchmark source code via GitHub
• digital evolution benchmark source code via GitHub
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• 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

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

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• source archive via Zenodo z

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

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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

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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

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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

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

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• source archive via Zenodo z

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

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

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

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• source archive via Zenodo z

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

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• 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

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

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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

• documentation via ReadTheDocs 📖

• documentation via ReadTheDocs 📖

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

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• source archive via Zenodo z

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

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

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.

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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

• documentation via ReadTheDocs 📖

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

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• poster via Google Slides

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

• 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

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• poster 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

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• LaTeX source via GitHub
• primary publication

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

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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

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

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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.

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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

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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

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