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Title: On the constructive power of ecology in open-ended evolving systems
Creator: Dolson, Emily L.
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Ecology is a powerful force for unlocking the full potential of evolution. Ecological interactions create feedback loops that promote diversification, both in natural ecosystems and in more applied evolutionary computation frameworks. However, we currently lack a strong theoretical framework that predicts how a given ecological community will evolve. Such a framework would allow us to better understand and anticipate change in evolving systems and facilitate the harnessing of ecology as a...
Show more"Ecology is a powerful force for unlocking the full potential of evolution. Ecological interactions create feedback loops that promote diversification, both in natural ecosystems and in more applied evolutionary computation frameworks. However, we currently lack a strong theoretical framework that predicts how a given ecological community will evolve. Such a framework would allow us to better understand and anticipate change in evolving systems and facilitate the harnessing of ecology as a tool for guiding evolution. In this work, I develop tools to begin the development of such theory in computational systems. Using these tools, I show that different mechanisms for creating ecology (e.g. spatial structure and varied competition schemes) produce radically different community structures and evolutionary outcomes. I explore the implications of these differences in the context of evolutionary computation and biology."--Page ii.
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Title: An agent model of vertical integration in telecommunications and content
Creator: Koning, Kendall Jay
Date: 2017
Collection: Electronic Theses & Dissertations
Description: "This dissertation explores several important telecommunications policy issues in light of recent developments in the wireline broadband and online video markets." -- Abstract.

Title: Using evolutionary approach to optimize and model multi-scenario, multi-objective fault-tolerant problems
Creator: Zhu, Ling (Engineer)
Date: 2017
Collection: Electronic Theses & Dissertations
Description: Fault-tolerant design involves different scenarios, such as scenarios with no fault in the system, with faults occurring randomly, with different operation conditions, and with different loading conditions. For each scenario, there can be multiple requirements (objectives). To assess the performance of a design (solution), it needs to be evaluated over a number of different scenarios containing various requirements in each scenario. We consider this problem as a multi-scenario, multi...
Show moreFault-tolerant design involves different scenarios, such as scenarios with no fault in the system, with faults occurring randomly, with different operation conditions, and with different loading conditions. For each scenario, there can be multiple requirements (objectives). To assess the performance of a design (solution), it needs to be evaluated over a number of different scenarios containing various requirements in each scenario. We consider this problem as a multi-scenario, multi-objective (MSMO) problem.Despite its practical importance and prevalence in engineering application, there are not many studies which systematically solve the MSMO problem. In this dissertation, we focus on optimizing and modeling MSMO problems, and propose various approaches to solve different types of MSMO optimization problems, especially multi-objective fault-tolerant problems. We classify MSMO optimization problem into two categories: scenario-dependent and scenario-independent. For the scenario-dependent MSMO problem, we review existing methodologies and suggest two evolutionary-based methods for handling multiple scenarios and objectives: aggregated method and integrated method. The effectiveness of both methods are demonstrated on several case studies including numerical problems and engineering design problems. The engineering problems include cantilever-type welded beam design, truss bridge design, four-bar truss design. The experimental results show that both methods can find a set of widely distributed solutions that are compromised among the respective objective values under all scenarios. We also model fault-tolerant programs using the aggregated method. We synthesize three fault-tolerant distributed programs: Byzantine agreement program, token ring circulation program and consensus program with failure detector $S$. The results show that evolutionary-base MSMO approach, as a generic method, can effectively model fault-tolerant programs. For the scenario-independent MSMO problem, we apply evolutionary multi-objective approach. As a case study, we optimize a probabilistic self-stabilizing program, a special type of fault-tolerant program, and obtain several interesting counter-intuitive observations under different scenarios.
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Title: Harnessing evolutionary computation for the design and generation of adaptive embedded controllers within the context of uncertainty
Creator: Byers, Chad Michael
Date: 2015
Collection: Electronic Theses & Dissertations
Description: A critical challenge for the design of embedded controllers is incorporating desirable qualities such as robustness, fault tolerance, and adaptability into the control process in order to respond to dynamic environmental conditions. An embedded controller governs the execution of a task-specific system by monitoring information from its environment via sensors and producing an appropriate response through the system's actuators, often independent of any supervisory control. For a human...
Show moreA critical challenge for the design of embedded controllers is incorporating desirable qualities such as robustness, fault tolerance, and adaptability into the control process in order to respond to dynamic environmental conditions. An embedded controller governs the execution of a task-specific system by monitoring information from its environment via sensors and producing an appropriate response through the system's actuators, often independent of any supervisory control. For a human developer, identifying the set of all possible combinations of conditions a system might experience and designing a solution to accommodate this set is burdensome, costly, and often, infeasible. To alleviate this burden, a variety of techniques have been explored to automate the generation of embedded controller solutions. In this dissertation, we focus on the bio-inspired technique referred to as evolutionary computation where we harness evolution's power as a population-based, global search technique to build up good behavioral components. In this way, evolution naturally selects for these desirable qualities in order for a solution to remain competitive over time in the population. Often, these search techniques operate in the context of uncertainty where aspects of the (1) problem domain, (2) solution space, and (3) search process itself are subject to variation and change. To mitigate issues associated with uncertainty in the problem domain, we propose the digital enzyme, a biologically-inspired model that maps the complexity of both the environment and the system into the space of values rather than instructions. To address uncertainty in the solution space, we remove constraints in our initial digital enzyme model to allow the genome structure to be dynamic and open-ended, accommodating a wider range of evolved solution designs. Finally, to help explore the inherent uncertainty that exists in the search process, we uncover a hidden feature interaction present between the diversity-preserving search operator of a popular evolutionary algorithm and propose a new way to use niching as a means to mitigate its unwanted effects and bias on search.
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Title: Mechanisms of adaptation and speciation : an experimental study using artificial life
Creator: Anderson, Carlos Jesus
Date: 2013
Collection: Electronic Theses & Dissertations
Description: Detailed experimental studies in evolutionary biology are sometimes difficult--even with model organisms. Theoretical models alleviate some of these difficulties and often provide clean results, but they cannot always capture the complexity of dynamic evolutionary processes. Artificial life systems are tools that fall somewhere between model organisms and theoretical models that have been successfully used to study evolutionary biology. These systems simulate simple organisms that replicate,...
Show moreDetailed experimental studies in evolutionary biology are sometimes difficult--even with model organisms. Theoretical models alleviate some of these difficulties and often provide clean results, but they cannot always capture the complexity of dynamic evolutionary processes. Artificial life systems are tools that fall somewhere between model organisms and theoretical models that have been successfully used to study evolutionary biology. These systems simulate simple organisms that replicate, acquire random mutations, and reproduce differentially; as a consequence, they evolve naturally (i.e., evolution itself is not simulated). Here I use the software Avida to study several open questions on the genetic mechanisms of adaptation and speciation.In Chapter 1 (p. 13), I investigated whether beneficial alleles during adaptation came from new mutations or standing genetic variation--alleles already present in the population. I found that most beneficial alleles came from standing genetic variation, but new mutations were necessary for long-term evolution. I also found that adaptation from standing genetic variation was faster than from new mutations. Finally, I found that recombination brought together beneficial combinations of alleles from standing genetic variation.In Chapter 2 (p. 31), I investigated the probability of compensatory adaptation vs. reversion. Compensatory adaptation is the fixation of mutations that ameliorate the effects of deleterious mutations while the original deleterious mutations remain fixed. I found that compensatory adaptation was very common, but the window of opportunity for reversion was increased when the initial fitness of the population was high, the population size was large, and the mutation rate was high. The reason that the window of opportunity for reversion was constrained was that negative epistatic interactions with compensatory mutations prevented the revertant from being beneficial to the population.In Chapter 3 (p. 58), I showed experimentally that compensatory adaptation can lead to reproductive isolation (specifically, postzygotic isolation). In addition, I found that the strength of this isolation was independent of the effect size of the original deleterious mutations. Finally, I found that both deleterious and compensatory mutations contribute equally to reproductive isolation.Reproductive isolation between populations often evolves as a byproduct of independent adaptation to new environments, but the selective pressures of these environments may be divergent (`ecological speciation') or uniform (`mutation-order speciation'). In Chapter 4 (p. 75), I compared directly the strength of postzygotic isolation generated by ecological and mutation-order processes with and without migration. I found that ecological speciation generally formed stronger isolation than mutation-order speciation and that mutation-order speciation was more sensitive to migration than ecological speciation.Under the Dobzhansky-Muller model of speciation, hybrid inviability or sterility results from the evolution of genetic incompatibilities (DMIs) between species-specific alleles. This model predicts that the number of pairwise DMIs between species should increase quadratically through time, but the few tests of this `snowball effect' have had conflicting results. In Chapter 5 (p. 101), I show that pairwise DMIs accumulated quadratically, supporting the snowball effect. I found that more complex genetic interactions involved alleles that rescued pairwise incompatibilities, explaining the discrepancy between the expected accumulations of DMIs and observation.
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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: Using evolutionary computation to automatically refractor software designs to include design patterns
Creator: Jensen, Adam C.
Date: 2010
Collection: Electronic Theses & Dissertations

Title: Natural niching : applying ecological principles to evolutionary computation
Creator: Goings, Sherri
Date: 2010
Collection: Electronic Theses & Dissertations

Title: Evolving artificial neural networks with generative encodings inspired by developmental biology
Creator: Clune, Jeff
Date: 2010
Collection: Electronic Theses & Dissertations

Title: Sustainable evolutionary algorithms and scalable evolutionary synthesis of dynamic systems
Creator: Hu, Jianjun
Date: 2004
Collection: Electronic Theses & Dissertations

Title: Achieving consistent evolution across isometrically equivalent search spaces
Creator: Patton, Arnold L.
Date: 2004
Collection: Electronic Theses & Dissertations

Title: Design automation of mechatronic systems using evolutionary computation and bond graph
Creator: Fan, Zhun
Date: 2004
Collection: Electronic Theses & Dissertations

Title: Enhancing pattern recognition using evolutionary computation for feature selection and extraction with application to the biochemistry of protein-water binding
Creator: Raymer, Michael L.
Date: 2000
Collection: Electronic Theses & Dissertations

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