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Unicellular organisms can engage in a process by which a cell purposefully destroys itself, termed programmed cell death (PCD). While it is clear that the death of specific cells within amulticellularorganism could increase inclusive fitness (e.g., during development), the origin of PCD inunicellularorganisms is less obvious. Kin selection has been shown to help maintain instances of PCD in existing populations of unicellular organisms; however, competing hypotheses exist about whether additional factors are necessary to explain its origin. Those factors could include an environmental shift that causes latent PCD to be expressed, PCD hitchhiking on a large beneficial mutation, and PCD being simply a common pathology. Here, we present results using an artificial life model to demonstrate that kin selection can, in fact, be sufficient to give rise to PCD in unicellular organisms. Furthermore, when benefits to kin are direct—that is, resources provided to nearby kin—PCD is more beneficial than when benefits are indirect—that is, nonkin are injured, thus increasing the relative amount of resources for kin. Finally, when considering how strict organisms are in determining kin or nonkin (in terms of mutations), direct benefits are viable in a narrower range than indirect benefits.

This article has been awarded Open Data and Open Materials Badges. All materials and data are publicly accessible via the Open Science Framework athttps://github.com/anyaevostinar/SuicidalAltruismDissertation/tree/master/LongTerm.

 

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

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. 

In digital evolution, populations of computational organisms evolve via the same principles that govern natural selection in nature. These platforms have been used to great effect as a controlled system in which to conduct evolutionary experiments and develop novel evolutionary theory. In addition to their complex evolutionary dynamics, many digital evolution systems also produce rich ecological communities. As a result, digital evolution is also a powerful tool for research on eco-evolutionary dynamics. Here, we review the research to date in which digital evolution platforms have been used to address eco-evolutionary (and in some cases purely ecological) questions. This work has spanned a wide range of topics, including competition, facilitation, parasitism, predation, and macroecological scaling laws. We argue for the value of further ecological research in digital evolution systems and present some particularly promising directions for further research. 

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. 

Fluctuating environmental conditions are ubiquitous in natural systems, and populations have evolved various strategies to cope with such fluctuations. The particular mechanisms that evolve profoundly influence subsequent evolutionary dynamics. One such mechanism is phenotypic plasticity, which is the ability of a single genotype to produce alternate phenotypes in an environmentally dependent context. Here, we use digital organisms (self-replicating computer programs) to investigate how adaptive phenotypic plasticity alters evolutionary dynamics and influences evolutionary outcomes in cyclically changing environments. Specifically, we examined the evolutionary histories of both plastic populations and non-plastic populations to ask: (1) Does adaptive plasticity promote or constrain evolutionary change? (2) Are plastic populations better able to evolve and then maintain novel traits? And (3), how does adaptive plasticity affect the potential for maladaptive alleles to accumulate in evolving genomes? We find that populations with adaptive phenotypic plasticity undergo less evolutionary change than non-plastic populations, which must rely on genetic variation from de novo mutations to continuously readapt to environmental fluctuations. Indeed, the non-plastic populations undergo more frequent selective sweeps and accumulate many more genetic changes. We find that the repeated selective sweeps in non-plastic populations drive the loss of beneficial traits and accumulation of maladaptive alleles, whereas phenotypic plasticity can stabilize populations against environmental fluctuations. This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts. In general, the evolution of adaptive phenotypic plasticity shifted evolutionary dynamics to be more similar to that of populations evolving in a static environment than to non-plastic populations evolving in an identical fluctuating environment. All natural environments subject populations to some form of change; our findings suggest that the stabilizing effect of phenotypic plasticity plays an important role in subsequent adaptive evolution. 

Fine-scale evolutionary dynamics can be challenging to tease out when focused on the broad brush strokes of whole populations over long time spans. We propose a suite of diagnostic analysis techniques that operate on lineages and phylogenies in digital evolution experiments, with the aim of improving our capacity to quantitatively explore the nuances of evolutionary histories in digital evolution experiments. We present three types of lineage measurements: lineage length, mutation accumulation, and phenotypic volatility. Additionally, we suggest the adoption of four phylogeny measurements from biology: phylogenetic richness, phylogenetic divergence, phylogenetic regularity, and depth of the most-recent common ancestor. In addition to quantitative metrics, we also discuss several existing data visualizations that are useful for understanding lineages and phylogenies: state sequence visualizations, fitness landscape overlays, phylogenetic trees, and Muller plots. We examine the behavior of these metrics (with the aid of data visualizations) in two well-studied computational contexts: (1) a set of two-dimensional, real-valued optimization problems under a range of mutation rates and selection strengths, and (2) a set of qualitatively different environments in the Avida digital evolution platform. These results confirm our intuition about how these metrics respond to various evolutionary conditions and indicate their broad value. 

Lexicase selection has been proven highly successful for finding effective solutions to problems in genetic programming, especially for test-based problems where there are many distinct test cases that must all be passed. However, lexicase (as with most selection schemes) requires all prospective solutions to be evaluated against most test cases each generation, which can be computationally expensive. Here, we propose reducing the number of per-generation evaluations required by applying random subsampling: using a subset of test cases each generation (down-sampling) or by assigning test cases to subgroups of the population (cohort assignment). Tests are randomly reassigned each generation, and candidate solutions are only ever evaluated on test cases that they are assigned to, radically reducing the total number of evaluations needed while ensuring that each lineage eventually encounters all test cases. We tested these lexicase variants on five different program synthesis problems, across a range of down-sampling levels and cohort sizes. We demonstrate that these simple techniques to reduce the number of per-generation evaluations in lexicase can substantially improve overall performance for equivalent computational effort.