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Title: Evolution of distributed behavior
Creator: Knoester, David B.
Date: 2011
Collection: Electronic Theses & Dissertations
Description: In this dissertation, we describe a study in the evolution of distributed behavior, where evolutionary algorithms are used to discover behaviors for distributed computing systems. We define distributed behavior as that in which groups of individuals must both cooperate in working towards a common goal and coordinate their activities in a harmonious fashion. As such, communication among individuals is necessarily a key component of distributed behavior, and we have identified three classes of...
Show moreIn this dissertation, we describe a study in the evolution of distributed behavior, where evolutionary algorithms are used to discover behaviors for distributed computing systems. We define distributed behavior as that in which groups of individuals must both cooperate in working towards a common goal and coordinate their activities in a harmonious fashion. As such, communication among individuals is necessarily a key component of distributed behavior, and we have identified three classes of distributed behavior that require communication: data-driven behaviors, where semantically meaningful data is transmitted between individuals; temporal behaviors, which are based on the relative timing of individuals' actions; and structural behaviors, which are responsible for maintaining the underlying communication network connecting individuals. Our results demonstrate that evolutionary algorithms can discover groups of individuals that exhibit each of these different classes of distributed behavior, and that these behaviors can be discovered both in isolation (e.g., evolving a purely data-driven algorithm) and in concert (e.g., evolving an algorithm that includes both data-driven and structural behaviors). As part of this research, we show that evolutionary algorithms can discover novel heuristics for distributed computing, and hint at a new class of distributed algorithm enabled by such studies.The majority of this research was conducted with the Avida platform for digital evolution, a system that has been proven to aid researchers in understanding the biological process of evolution by natural selection. For this reason, the results presented in this dissertation provide the foundation for future studies that examine how distributed behaviors evolved in nature. The close relationship between evolutionary biology and evolutionary algorithms thus aids our study of evolving algorithms for the next generation of distributed computing systems.
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Title: The evolution of a key innovation in an experimental population of Escherichia coli : a tale of opportunity, contingency, and co-option
Creator: Blount, Zachary David
Date: 2011
Collection: Electronic Theses & Dissertations
Description: The importance of historical contingency in evolution has been extensively debated over the last few decades, but direct empirical tests have been rare. Twelve initially identical populations of E. coli were founded in 1988 to investigate this issue. They have since evolved for more than 50,000 generations in a glucose-limited medium that also contains a citrate. However, the inability to use citrate as a carbon source under oxic conditions is a species-defining trait of ...
Show moreThe importance of historical contingency in evolution has been extensively debated over the last few decades, but direct empirical tests have been rare. Twelve initially identical populations of E. coli were founded in 1988 to investigate this issue. They have since evolved for more than 50,000 generations in a glucose-limited medium that also contains a citrate. However, the inability to use citrate as a carbon source under oxic conditions is a species-defining trait of E. coli. A weakly Cit+ variant capable of aerobic citrate utilization finally evolved in one population just prior to 31,500 generations. Shortly after 33,000 generations, the population experienced a several-fold expansion as strongly Cit+ variants rose to numerical dominance (but not fixation). The Cit+ trait was therefore a key innovation that increased both population size and diversity by opening a previously unexploited ecological opportunity.The long-delayed and unique evolution of the Cit+ innovation might be explained by two possible hypotheses. First, evolution of the Cit+ function may have required an extremely rare mutation. Alternately, the evolution of Cit+ may have been contingent upon one or more earlier mutations that had accrued over the population's history. I tested these hypotheses in a series of experiments in which I "replayed" evolution from different points in the population's history. I observed no Cit+ mutants among 8.4 x 1012 ancestral cells, nor among 9 x 1012 cells from 60 clones sampled in the first 15,000 generations. However, I observed a significantly greater tendency to evolve Cit+ among later clones. These results indicate that one or more earlier mutations potentiated the evolution of Cit+ by increasing the rate of mutation to Cit+ to an accessible, though still very low, level. The evolution of the Cit+ function was therefore contingent on the particular history of the population in which it occurred.I investigated the Cit+ innovation's history and genetic basis by sequencing the genomes of 29 clones isolated from the population at various time points. Analysis of these genomes revealed that at least 3 distinct clades coexisted for more than 10,000 generations prior to the innovation's evolution. The Cit+ trait originated in one clade by a tandem duplication that produced a new regulatory module in which a silent citrate transporter was placed under the control of an aerobically-expressed promoter. Subsequent increases in the copy number of this new regulatory module refined the initially weak Cit+ phenotype, leading to the population expansion. The 3 clades varied in their propensity to evolve the novel Cit+ function, though genotypes able to do so existed in all 3, implying that potentiation involved multiple mutations.My findings demonstrate that historical contingency can significantly impact evolution, even under the strictest of conditions. Moreover, they suggest that contingency plays an especially important role in the evolution of novel innovations that, like Cit+, require prior construction of a potentiating genetic background, and are thus not easily evolved by gradual, cumulative selection. Contingency may therefore have profoundly shaped life's evolution given the importance of evolutionary novelties in the history of life. Finally, the genetic basis of the Cit+ function illustrates the importance of promoter capture and altered gene regulation in mediation the exaptation events that often underlie evolutionary innovations.
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Title: Applying evolutionary computation techniques to address environmental uncertainty in dynamically adaptive systems
Creator: Ramirez, Andres J.
Date: 2013
Collection: Electronic Theses & Dissertations
Description: A dynamically adaptive system (DAS) observes itself and its execution environment at run time to detect conditions that warrant adaptation. If an adaptation is necessary, then a DAS changes its structure and/or behavior to continuously satisfy its requirements, even as its environment changes. It is challenging, however, to systematically and rigorously develop a DAS due to environmental uncertainty. In particular, it is often infeasible for a human to identify all possible combinations of...
Show moreA dynamically adaptive system (DAS) observes itself and its execution environment at run time to detect conditions that warrant adaptation. If an adaptation is necessary, then a DAS changes its structure and/or behavior to continuously satisfy its requirements, even as its environment changes. It is challenging, however, to systematically and rigorously develop a DAS due to environmental uncertainty. In particular, it is often infeasible for a human to identify all possible combinations of system and environmental conditions that a DAS might encounter throughout its lifetime. Nevertheless, a DAS must continuously satisfy its requirements despite the threat that this uncertainty poses to its adaptation capabilities. This dissertation proposes a model-based framework that supports the specification, monitoring, and dynamic reconfiguration of a DAS to explicitly address uncertainty. The proposed framework uses goal-oriented requirements models and evolutionary computation techniques to derive and fine-tune utility functions for requirements monitoring in a DAS, identify combinations of system and environmental conditions that adversely affect the behavior of a DAS, and generate adaptations on-demand to transition the DAS to a target system configuration while preserving system consistency. We demonstrate the capabilities of our model-based framework by applying it to an industrial case study involving a remote data mirroring network that efficiently distributes data even as network links fail and messages are dropped, corrupted, and delayed.
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Title: Experimental evolution and ecological consequences : new niches and changing stoichiometry
Creator: Turner, Caroline B.
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Evolutionary change can alter the ecological conditions in which organisms live and continue to evolve. My dissertation research used experimental evolution to study two aspects of evolutionary change with ecological consequences: the generation of new ecological niches and evolution of the elemental composition of biomass. I worked with the long-term evolution experiment (LTEE), which is an ongoing experiment in which E. coli have evolved under laboratory conditions for more than 60,000...
Show moreEvolutionary change can alter the ecological conditions in which organisms live and continue to evolve. My dissertation research used experimental evolution to study two aspects of evolutionary change with ecological consequences: the generation of new ecological niches and evolution of the elemental composition of biomass. I worked with the long-term evolution experiment (LTEE), which is an ongoing experiment in which E. coli have evolved under laboratory conditions for more than 60,000 generations. The LTEE began with extremely simple ecological conditions. Twelve populations were founded from a single bacterial genotype and growth was limited by glucose availability. In Chapter 1, I focused on a population within the LTEE in which some of the bacteria evolved the ability to consume a novel resource, citrate. Citrate was present in the growth media throughout the experiment, but E. coli is normally unable to consume it under aerobic conditions. The citrate consumers (Cit+) coexisted with a clade of bacteria which were unable to consume citrate (Cit-). Specialization on glucose, the standard carbon source in the LTEE, was insufficient to explain the frequency-dependent coexistence of Cit- with Cit+. Instead Cit– evolved to cross-feed on molecules released by Cit+. The evolutionary innovation of citrate consumption led to a more complex ecosystem in which two co-existing ecotypes made use of five different carbon sources.After 10,000 generations of coexistence, Cit- went extinct from the population (Chapter 2). I conducted replay experiments, re-evolving for 500 generations 20 replicate populations from prior to extinction. Cit- was retained in all populations, indicating that the extinction was not deterministic. Furthermore, when I added small numbers of Cit- to the population after extinction, Cit- was able to reinvade. It therefore appears that the Cit- extinction was not due to exclusion by Cit+, but rather to unknown laboratory variation.Chapter 3 shifts focus to studying evolutionary changes in stoichiometry, the ratio of different elements within organisms’ biomass. Variation in stoichiometry between organisms has important ecological consequences, but the evolutionary origin of that variation had not previously been studied experimentally. Growth in the LTEE is carbon limited and nitrogen and phosphorus are abundant. Additionally, daily transfer to fresh media selects for increased growth rate, which other research has suggested correlates to higher phosphorus content. Consistent with our predictions based on this environment, clones isolated after 50,000 generations of evolution had significantly higher nitrogen and phosphorus content than ancestral clones. There was no change in the proportion of carbon in biomass, but the total amount of carbon retained in biomass increased, indicating that the bacteria also evolved higher carbon use efficiency.To test whether the increases in nitrogen and phosphorus observed in the LTEE were a result of carbon limitation or were side effects of other selective factors in the experiment, I evolved clones from the LTEE for 1000 generations under nitrogen rather than carbon limitation (Chapter 4). The stoichiometry of the bacteria did change over the course of 1000 generations, indicating that evolution of stoichiometry can occur over relatively short time frames. Unexpectedly however, the evolved bacteria had higher nitrogen and phosphorus content. It appears that the bacteria were initially poor at incorporating nitrogen into biomass, but evolved improved nitrogen uptake.
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Title: Elucidating the evolutionary origins of collective animal behavior
Creator: Olson, Randal S.
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Despite over a century of research, the evolutionary origins of collective animal behavior remain unclear. Dozens of hypotheses explaining the evolution of collective behavior have risen and fallen in the past century, but until recently it has been difficult to perform controlled behavioral evolution experiments to isolate these various hypotheses and test their individual effects. In this dissertation, I outline a relatively new method using digital models of evolution to perform controlled...
Show moreDespite over a century of research, the evolutionary origins of collective animal behavior remain unclear. Dozens of hypotheses explaining the evolution of collective behavior have risen and fallen in the past century, but until recently it has been difficult to perform controlled behavioral evolution experiments to isolate these various hypotheses and test their individual effects. In this dissertation, I outline a relatively new method using digital models of evolution to perform controlled behavioral evolution experiments. In particular, I use these models to directly explore the evolutionary consequence of the selfish herd, predator confusion, and the many eyes hypotheses, and demonstrate how the models can lend key insights useful to behavioral biologists, computer scientists, and robotics researchers. This dissertation lays the groundwork for the experimental study of the hypotheses surrounding the evolution of collective animal behavior, and establishes a path for future experiments to explore and disentangle how the various hypothesized benefits of collective behavior interact over evolutionary time.
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Title: Evolution of cooperation in the light of information theory
Creator: Mirmomeni, Masoud
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Cooperation is ubiquitous in different biological levels and is necessary for evolution to shape the life and create new forms of organization. Genes cooperate in controlling cells; cells efficiently collaborate together to produce cohesive multi-cellular organisms; members of insect colonies and animal clans cooperate in protecting the colony and providing food. Cooperation means that members of a group bear a cost, c, for another individuals to earn a benefit, b. While cooperators of the...
Show moreCooperation is ubiquitous in different biological levels and is necessary for evolution to shape the life and create new forms of organization. Genes cooperate in controlling cells; cells efficiently collaborate together to produce cohesive multi-cellular organisms; members of insect colonies and animal clans cooperate in protecting the colony and providing food. Cooperation means that members of a group bear a cost, c, for another individuals to earn a benefit, b. While cooperators of the group help others by paying a cost, defectors receive the benefits of this altruistic behavior without providing any service in return to the group. To address this dilemma, here we use a game theoretic approach to model and study evolutionary dynamics that can lead to unselfish behavior. Evolutionary game theory is an approach to study frequency-dependent systems. In evolutionary games the fitness of individuals depends on the relative abundance of the various types in the population. We explore different strategies and different games such as iterated games between players with conditional strategies, multi player games, and iterated games between fully stochastic strategies in noisy environments to find the necessity conditions that lead to cooperation. Interestingly, we see that in all of these games communication is the key factor for maintaining cooperation among selfish individuals. We show that communication and information exchange is necessary for the emergence of costly altruism, and to maintain cooperation in the group there should be minimum rate of communication between individuals. We quantify this minimum amount of information exchange, which is necessary for individuals to exhibit cooperative behavior, by defining a noisy communication channel between them in iterated stochastic games and measuring the communication rate (in bits) during the break down of cooperation.
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Title: An analysis of fitness in long-term asexual evolution experiments
Creator: Wiser, Michael J.
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Evolution is the central unifying concept of modern biology. Yet it can be hard to study in natural system, as it unfolds across generations. Experimental evolution allows us to ask questions about the process of evolution itself: How repeatable is the evolutionary process? How predictable is it? How general are the results? To address these questions, my collaborators and I carried out experiments both within the Long-Term Evolution Experiment (LTEE) in the bacteria Escherichia coli, and the...
Show moreEvolution is the central unifying concept of modern biology. Yet it can be hard to study in natural system, as it unfolds across generations. Experimental evolution allows us to ask questions about the process of evolution itself: How repeatable is the evolutionary process? How predictable is it? How general are the results? To address these questions, my collaborators and I carried out experiments both within the Long-Term Evolution Experiment (LTEE) in the bacteria Escherichia coli, and the digital evolution software platform Avida. In Chapter 1, I focused on methods. Previous research in the LTEE has relied on one particular way of measuring fitness, which we know becomes less precise as fitness differentials increase. I therefore decided to test whether two alternate ways of measuring fitness would improve precision, using one focal population. I found that all three methods yielded similar results in both fitness and coefficient of variation, and thus we should retain the traditional method.In Chapter 2, I turned to measuring fitness in each of the populations. Previous work had considered fitness to change as a hyperbola. A hyperbolic function is bounded, and predicts that fitness will asymptotically approach a defined upper bound; however, we knew that fitness in these populations routinely exceeded the asymptotic limit calculated from a hyperbola fit to the earlier data. I instead used to a power law, a mathematical function that does not have an upper bound. I found that this function substantially better describes fitness in this system, both among the whole set of populations, and in most of the individual populations. I also found that the power law models fit on just early subsets of the data accurately predict fitness far into the future. This implies that populations, even after 50,000 generations of evolution in consistent environment, are so far from the tops of fitness peaks that we cannot detect evidence of those peaks.In Chapter 3, I examined to how variance in fitness changes over long time scales. The among-population variance over time provides us information about the adaptive landscape on which the populations have been evolving. I found that among-population variance remains significant. Further, competitions between evolved pairs of populations reveal additional details about fitness trajectories than can be seen from competitions against the ancestor. These results demonstrate that our populations have been evolving on a complex adaptive landscape.In Chapter 4, I examined whether the patterns found in Chapter 2 apply to a very different evolutionary system, Avida. This system incorporates many similar evolutionary pressures as the LTEE, but without the details of cellular biology that underlie nearly all organic life. I find that in both the most complex and simplest environments in Avida, fitness also follows the same power law dynamics as seen in the LTEE. This implies that power law dynamics may be a general feature of evolving systems, and not dependent on the specific details of the system being studied.
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Title: Out of the box optimization using the parameter-less population pyramid
Creator: Goldman, Brian W.
Date: 2015
Collection: Electronic Theses & Dissertations
Description: The Parameter-less Population Pyramid (P3) is a recently introduced method for performing evolutionary optimization without requiring any user-specified parameters. P3’s primary innovation is to replace the generational model with a pyramid of multiple populations that are iteratively created and expanded. In combination with local search and advanced crossover, P3 scales to problem difficulty, exploiting previously learned information before adding more diversity.Across seven problems, each...
Show moreThe Parameter-less Population Pyramid (P3) is a recently introduced method for performing evolutionary optimization without requiring any user-specified parameters. P3’s primary innovation is to replace the generational model with a pyramid of multiple populations that are iteratively created and expanded. In combination with local search and advanced crossover, P3 scales to problem difficulty, exploiting previously learned information before adding more diversity.Across seven problems, each tested using on average 18 problem sizes, P3 outperformed all five advanced comparison algorithms. This improvement includes requiring fewer evaluations to find the global optimum and better fitness when using the same number of evaluations. Using both algorithm analysis and comparison we show P3’s effectiveness is due to its ability to properly maintain, add, and exploit diversity. Unlike the best comparison algorithms, P3 was able to achieve this quality without any problem-specific tuning. Thus, unlike previous parameter-less methods, P3 does not sacrifice quality for applicability. Therefore we conclude that P3 is an efficient, general, parameter-less approach to black-box optimization that is more effective than existing state-of-the-art techniques.Furthermore, P3 can be specialized for gray-box problems, which have known, limited, non-linear relationships between variables. Gray-Box P3 leverages the Hamming-Ball Hill Climber, an exceptionally efficient form of local search, as well as a novel method for performing crossover using the known variable interactions. In doing so Gray-Box P3 is able to find the global optimum of large problems in seconds, improving over Black-Box P3 by up to two orders of magnitude.
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Title: Automatically addressing uncertainty in autonomous robots with computational evolution
Creator: Clark, Anthony Joseph
Date: 2016
Collection: Electronic Theses & Dissertations
Description: Autonomous robotic systems are becoming prevalent in our daily lives. Many robots are still restricted to manufacturing settings where precision and repetition are paramount. However, autonomous devices are increasingly being designed for applications such as search and rescue, remote sensing, and tasks considered too dangerous for people. In these cases, it is crucial to continue operation even when some unforeseen adversity decreases performance levels---a robot with diminished performance...
Show moreAutonomous robotic systems are becoming prevalent in our daily lives. Many robots are still restricted to manufacturing settings where precision and repetition are paramount. However, autonomous devices are increasingly being designed for applications such as search and rescue, remote sensing, and tasks considered too dangerous for people. In these cases, it is crucial to continue operation even when some unforeseen adversity decreases performance levels---a robot with diminished performance is still successful if it is able to deal with uncertainty, which includes any unexpected change due to unmodeled dynamics, changing control strategies, or changes in functionality resulting from damage or aging.The research presented in this dissertation seeks to improve such autonomous systems through three evolution-based techniques. First, robots are optimized offline so that they best exploit available material characteristics, for instance flexible materials, with respect to multiple objectives (e.g., speed and efficiency). Second, adaptive controllers are evolved, which enable robots to better respond to unforeseen changes to themselves and their environments. Finally, adaptation limits are discovered using a proposed mode discovery algorithm. Once the boundaries of adaptation are known, self-modeling is applied online to determine the current operating mode and select/generate an appropriate controller.These three techniques work together to create a holistic method, which will enable autonomous robotic systems to automatically handle uncertainty. The proposed methods are evaluated using robotic fish as a test platform. Such systems can benefit in multiple ways from the integration of flexible materials. Moreover, robotic fish operate in complex, nonlinear environments, enabling thorough testing of the proposed methods.
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Title: Hybrid structural and behavioral diversity techniques for effective genetic programming
Creator: Burks, Armand Rashad
Date: 2017
Collection: Electronic Theses & Dissertations
Description: "Sustaining the diversity of evolving populations is a fundamental issue in genetic programming. We describe a novel measure of structural diversity for tree-based genetic programming, and we demonstrate its utility compared to other diversity techniques. We demonstrate our technique on the real-world application of tuberculosis screening from X-ray images. We then introduce a new paradigm of genetic programming that involves simultaneously maintaining structural and behavioral diversity in...
Show more"Sustaining the diversity of evolving populations is a fundamental issue in genetic programming. We describe a novel measure of structural diversity for tree-based genetic programming, and we demonstrate its utility compared to other diversity techniques. We demonstrate our technique on the real-world application of tuberculosis screening from X-ray images. We then introduce a new paradigm of genetic programming that involves simultaneously maintaining structural and behavioral diversity in order to further improve the efficiency of genetic programming. Our results show that simultaneously promoting structural and behavioral diversity improves genetic programming by leveraging the benefits of both aspects of diversity while overcoming the shortcomings of either technique in isolation. The hybridization increases the behavioral diversity of our structural diversity technique, and increases the structural diversity of the behavioral diversity techniques. This increased diversity leads to performance gains compared to either technique in isolation. We found that in many cases, our structural diversity technique provides significant performance improvement compared to other state-of-the-art techniques. Our results from the experiments comparing the hybrid techniques indicate that the largest performance gain was typically attributed to our structural diversity technique. The incorporation of the behavioral diversity techniques provide additional improvement in many cases."--Page ii.
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Title: A differential evolution approach to feature selection in genomic prediction
Creator: Whalen, Ian
Date: 2018
Collection: Electronic Theses & Dissertations
Description: The use of genetic markers has become widespread for prediction of genetic merit in agricultural applications and is a beginning to show promise for estimating propensity to disease in human medicine. This process is known as genomic prediction and attempts to model the mapping between an organism's genotype and phenotype. In practice, this process presents a challenging problem. Sequencing and recording phenotypic traits are often expensive and time consuming. This leads to datasets often...
Show moreThe use of genetic markers has become widespread for prediction of genetic merit in agricultural applications and is a beginning to show promise for estimating propensity to disease in human medicine. This process is known as genomic prediction and attempts to model the mapping between an organism's genotype and phenotype. In practice, this process presents a challenging problem. Sequencing and recording phenotypic traits are often expensive and time consuming. This leads to datasets often having many more features than samples. Common models for genomic prediction often fall victim to overfitting due to the curse of dimensionality. In this domain, only a fraction of the markers that are present significantly affect a particular trait. Models that fit to non-informative markers are in effect fitting to statistical noise, leading to a decrease in predictive performance. Therefore, feature selection is desirable to remove markers that do not appear to have a significant effect on the trait being predicted. The method presented here uses differential evolution based search for feature selection. This study will characterize differential evolution's efficacy in feature selection for genomic prediction and present several extensions to the base search algorithm in an attempt to apply domain knowledge to guide the search toward better solutions.
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Title: Optimizing for Mental Representations in the Evolution of Artificial Cognitive Systems
Creator: Kirkpatrick, Douglas Andrew
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Mental representations, or sensor-independent internal models of the environment, are used to interpret the world and make decisions based upon that understanding. For example, a human sees dark clouds in the sky, recalls that often dark clouds mean rain (a mental representation), and consequently decides to wear a raincoat. I seek to identify, understand, and encourage the evolution of these representations in silico. Previous work identified an information-theoretic tool, referred to as R,...
Show moreMental representations, or sensor-independent internal models of the environment, are used to interpret the world and make decisions based upon that understanding. For example, a human sees dark clouds in the sky, recalls that often dark clouds mean rain (a mental representation), and consequently decides to wear a raincoat. I seek to identify, understand, and encourage the evolution of these representations in silico. Previous work identified an information-theoretic tool, referred to as R, that measures mental representations in artificial cognitive systems (e.g., Markov Brains or Recurrent Neural Networks). Further work found that selecting for R, along with task performance, in the evolution of artificial cognitive systems leads to better overall performance on a given task. Here I explore the implications and opportunities of this modified selection process, referred to as R-augmentation. After an overview of common methods, techniques, and computational substrates in Chapter 1, a series of working chapters experimentally demonstrate the capabilities and possibilities of R-augmentation. First, in Chapter 2, I address concerns regarding potential limitations of R-augmentation. This includes an refutation of suspected negative impacts on the system’s ability to generalize within-domain and the system’s robustness to sensor noise. To the contrary, the systems evolved with R-augmentation tend to perform better than those evolved without, in the context of noisy environments and different computational components. In Chapter 3 I examine how R-augmentation works across different cognitive structures, focusing on the evolution of genetic programming related structures and the effect that augmentation has on the distribution of their representations. For Chapter 4, in the context of the all-component Markov Brain (referred to as a Buffet Brain, see [Hintze et al., 2019]) I analyze potential reasons that explain why R-augmentation works; the mechanism seems to be based on evolutionary dynamics as opposed to structural or component differences. Next, I demonstrate a novel usage of R-augmentation in Chapter 5; with R-augmentation, one can use far fewer training examples during evolution and the resulting systems still perform approximately as well as those that were trained on the full set of examples. This advantage in increased performance at low sample size is found in some examples of in-domain and out-domain generalization, with the ”worst-case” scenario being that the networks created by R-augmentation perform as well as their unaugmented equivalents. Lastly, in Chapter 6 I move beyond R-augmentation to explore using other neuro-correlates - particularly the distribution of representations, called smearedness - as part of the fitness function. I investigate the possibility of using MAP-Elites to identify an optimal value of smearedness for augmentation or for use as an optimization method in its own right. Taken together, these investigations demonstrate both the capabilities and limitations of R-augmentation, and open up pathways for future research.
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Title: The Evolution of Fundamental Neural Circuits for Cognition in Silico
Creator: Tehrani-Saleh, Ali
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Despite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms.In...
Show moreDespite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms.In this dissertation, I use a recently-developed neuroevolution platform -called Markov brain networks- in which Darwinian selection is used to evolve both structure and functionality of digital brains. I use this platform to study some of the most fundamental cognitive neural circuits: 1) visual motion detection, 2) collision-avoidance based on visual motion cues, 3) sound localization, and 4) time perception. In particular, I investigate both the selective pressures and environmental conditions in the evolution of these cognitive components, as well as the circuitry and computations behind them. This dissertation lays the groundwork for an evolutionary agent-based method to study the neural circuits for cognition in silico.
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Title: Replaying Life's Virtual Tape : Examining the Role of History in Experiments with Digital Organisms
Creator: Bundy, Jason Nyerere
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Evolution is a complex process with a simple recipe. Evolutionary change involves three essential “ingredients” interacting over many generations: adaptation (selection), chance (random variation), and history (inheritance). In 1989’s Wonderful Life, the late paleontologist Stephen Jay Gould advocated for the importance of historical contingency—the way unique events throughout history influence future possibilities—using a clever thought experiment of “replaying life’s tape”. But not...
Show moreEvolution is a complex process with a simple recipe. Evolutionary change involves three essential “ingredients” interacting over many generations: adaptation (selection), chance (random variation), and history (inheritance). In 1989’s Wonderful Life, the late paleontologist Stephen Jay Gould advocated for the importance of historical contingency—the way unique events throughout history influence future possibilities—using a clever thought experiment of “replaying life’s tape”. But not everyone was convinced. Some believed that chance was the primary driver of evolutionary change, while others insisted that natural selection was the most powerful influence. Since then, “replaying life’s tape” has become a core method in experimental evolution for measuring the relative contributions of adaptation, chance, and history. In this dissertation, I focus on the effects associated with history in evolving populations of digital organisms—computer programs that self-replicate, mutate, compete, and evolve in virtual environments. In Chapter 1, I discuss the philosophical significance of Gould’s thought experiment and its influence on experimental methods. I argue that his thought experiment was a challenge to anthropocentric reasoning about natural history that is still popular, particularly outside of the scientific community. In this regard, it was his way of advocating for a “radical” view of evolution. In Chapter 2—Richard Lenski, Charles Ofria, and I describe a two-phase, virtual, “long-term” evolution experiment with digital organisms using the Avida software. In Phase I, we evolved 10 replicate populations, in parallel, from a single genotype for around 65,000 generations. This part of the experiment is similar to the design of Lenski’s E. coli Long-term Evolution Experiment (LTEE). We isolated the dominant genotype from each population around 3,000 generations (shallow history) into Phase I and then again at the end of Phase I (deep history). In Phase II, we evolved 10 populations from each of the genotypes we isolated from Phase I in two new environments, one similar and one dissimilar to the old environment used for Phase I. Following Phase II, we estimated the contributions of adaptation, chance, and history to the evolution of fitness and genome length in each new environment. This unique experimental design allowed us to see how the contributions of adaptation, chance, and history changed as we extended the depth of history from Phase I. We were also able to determine whether the results depended on the extent of environmental change (similar or dissimilar new environment). In Chapter 3, we report an extended analysis of the experiment from the previous chapter to further examine how extensive adaptation to the Phase I environment shaped the evolution of replicates during Phase II. We show how the form of pleiotropy (antagonistic or synergistic) between the old (Phase I) and new (Phase II) habitats was influenced by the depth of history from Phase I (shallow or deep) and the extent of environmental change (similar or dissimilar new environment). In the final chapter Zachary Blount, Richard Lenski, and I describe an exercise we developed using the educational version of Avida (Avida-ED). The exercise features a two-phase, “replaying life’s tape” activity. Students are able to explore how the unique history of founders that we pre-evolved during Phase I influences the acquisition of new functions by descendent populations during Phase II, which the students perform during the activity.
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Title: EMERGENT COORDINATION : ADAPTATION, OPEN-ENDEDNESS, AND COLLECTIVE INTELLIGENCE
Creator: Bao, Honglin
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Agent-based modeling is a widely used computational method for studying the micro-macro bridge issue by simulating the microscopic interactions and observing the macroscopic emergence. This thesis begins with the fundamental methodology of agent-based models: how agents are represented, how agents interact, and how the agent population is structured. Two vital topics, the evolution of cooperation and opinion dynamics are used to illustrate methodological innovation. For the first topic, we...
Show moreAgent-based modeling is a widely used computational method for studying the micro-macro bridge issue by simulating the microscopic interactions and observing the macroscopic emergence. This thesis begins with the fundamental methodology of agent-based models: how agents are represented, how agents interact, and how the agent population is structured. Two vital topics, the evolution of cooperation and opinion dynamics are used to illustrate methodological innovation. For the first topic, we study the equilibrium selection in a coordination game in multi-agent systems. In particular, we focus on the characteristics of agents (supervisors and subordinates versus representative agents), the interactions of agents (reinforcement learning in the games with fixed versus adaptive learning rates according to the supervision and time-varying versus supervision-guided exploration rates), the network of agents (single-layer versus multi-layer networks), and their impact on the emergent behaviors. Regarding the second topic, we examine how opinions evolve and spread in a cognitively heterogeneous agent population with sparse interactions and how the opinion dynamics co-evolve with the open-ended society's structural change. We then discuss the rich insights into collective intelligence in the two proposed models viewed from the interaction-based adaptation and open-ended network structure. We finally link collective emergent intelligence to diverse applications in the realm of computing and other scientific fields in a cross-multidisciplinary manner.
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