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Full-Text Articles in Physical Sciences and Mathematics
Scheduling For Space Tracking And Heterogeneous Sensor Environments, Gabriel H. Greve
Scheduling For Space Tracking And Heterogeneous Sensor Environments, Gabriel H. Greve
Theses and Dissertations
This dissertation draws on the fields of heuristic and meta-heuristic algorithm development, resource allocation problems, and scheduling to address key Air Force problems. The world runs on many schedules. People depend upon them and expect these schedules to be accurate. A process is needed where schedules can be dynamically adjusted to allow tasks to be completed efficiently. For example, the Space Surveillance Network relies on a schedule to track objects in space. The schedule must use sensor resources to track as many high-priority satellites as possible to obtain orbit paths and to warn of collision paths. Any collisions that occurred …
Applying Models Of Circadian Stimulus To Explore Ideal Lighting Configurations, Alexander J. Price
Applying Models Of Circadian Stimulus To Explore Ideal Lighting Configurations, Alexander J. Price
Theses and Dissertations
Increased levels of time are spent indoors, decreasing human interaction with nature and degrading photoentrainment, the synchronization of circadian rhythms with daylight variation. Military imagery analysts, among other professionals, are required to work in low light level environments to limit power consumption or increase contrast on display screens to improve detail detection. Insufficient exposure to light in these environments results in inadequate photoentrainment which is associated with degraded alertness and negative health effects. Recent research has shown that both the illuminance (i.e., perceived intensity) and wavelength of light affect photoentrainment. Simultaneously, modern lighting technologies have improved our ability to construct …
Bit-Error-Rate-Minimizing Channel Shortening Using Post-Feq Diversity Combining And A Genetic Algorithm, Gokhan Altin
Bit-Error-Rate-Minimizing Channel Shortening Using Post-Feq Diversity Combining And A Genetic Algorithm, Gokhan Altin
Theses and Dissertations
In advanced wireline or wireless communication systems, i.e., DSL, IEEE 802.11a/g, HIPERLAN/2, etc., a cyclic prefix which is proportional to the channel impulse response is needed to append a multicarrier modulation (MCM) frame for operating the MCM accurately. This prefix is used to combat inter symbol interference (ISI). In some cases, the channel impulse response can be longer than the cyclic prefix (CP). One of the most useful techniques to mitigate this problem is reuse of a Channel Shortening Equalizer (CSE) as a linear preprocessor before the MCM receiver in order to shorten the effective channel length. Channel shortening filter …
Application Of Optimization Techniques To Spectrally Modulated, Spectrally Encoded Waveform Design, Todd W. Beard
Application Of Optimization Techniques To Spectrally Modulated, Spectrally Encoded Waveform Design, Todd W. Beard
Theses and Dissertations
A design process is demonstrated for a coexistent scenario containing Spectrally Modulated, Spectrally Encoded (SMSE) and Direct Sequence Spread Spectrum (DSSS) signals. Coexistent SMSE-DSSS designs are addressed under both perfect and imperfect DSSS code tracking conditions using a non-coherent delay-lock loop (DLL). Under both conditions, the number of SMSE subcarriers and subcarrier spacing are the optimization variables of interest. For perfect DLL code tracking conditions, the GA and RSM optimization processes are considered independently with the objective function being end-to-end DSSS bit error rate. A hybrid GA-RSM optimization process is used under more realistic imperfect DLL code tracking conditions. In …
Exploitation Of Self Organization In Uav Swarms For Optimization In Combat Environments, Dustin J. Nowak
Exploitation Of Self Organization In Uav Swarms For Optimization In Combat Environments, Dustin J. Nowak
Theses and Dissertations
This investigation focuses primarily on the development of effective target engagement for unmanned aerial vehicle (UAV) swarms using autonomous self-organized cooperative control. This development required the design of a new abstract UAV swarm control model which flows from an abstract Markov structure, a Partially Observable Markov Decision Process. Self-organization features, bio-inspired attack concepts, evolutionary computation (multi-objective genetic algorithms, differential evolution), and feedback from environmental awareness are instantiated within this model. The associated decomposition technique focuses on the iterative deconstruction of the problem domain state and dynamically building-up of self organizational rules as related to the problem domain environment. Resulting emergent …
Explicit Building Block Multiobjective Evolutionary Computation: Methods And Applications, Richard O. Day
Explicit Building Block Multiobjective Evolutionary Computation: Methods And Applications, Richard O. Day
Theses and Dissertations
This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Concentration is on concepts, design formulation, and prescription for multiobjective problem solving and explicit building block (BB) multiobjective evolutionary algorithms (MOEAs). Current state-of-the-art explicit BB MOEAs are addressed in the innovative design, execution, and testing of a new multiobjective explicit BB MOEA. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of effectiveness and efficiency. The main result of this research is the development of …
A Genetic Algorithm For Uav Routing Integrated With A Parallel Swarm Simulation, Matthew A. Russell
A Genetic Algorithm For Uav Routing Integrated With A Parallel Swarm Simulation, Matthew A. Russell
Theses and Dissertations
This research investigation addresses the problem of routing and simulating swarms of UAVs. Sorties are modeled as instantiations of the NP-Complete Vehicle Routing Problem, and this work uses genetic algorithms (GAs) to provide a fast and robust algorithm for a priori and dynamic routing applications. Swarms of UAVs are modeled based on extensions of Reynolds' swarm research and are simulated on a Beowulf cluster as a parallel computing application using the Synchronous Environment for Emulation and Discrete Event Simulation (SPEEDES). In a test suite, standard measures such as benchmark problems, best published results, and parallel metrics are used as performance …
Explicit Building-Block Multiobjective Genetic Algorithms: Theory, Analysis, And Developing, Jesse B. Zydallis
Explicit Building-Block Multiobjective Genetic Algorithms: Theory, Analysis, And Developing, Jesse B. Zydallis
Theses and Dissertations
This dissertation research emphasizes explicit Building Block (BB) based MO EAs performance and detailed symbolic representation. An explicit BB-based MOEA for solving constrained and real-world MOPs is developed the Multiobjective Messy Genetic Algorithm II (MOMGA-II) which is designed to validate symbolic BB concepts. The MOMGA-II demonstrates that explicit BB-based MOEAs provide insight into solving difficult MOPs that is generally not realized through the use of implicit BB-based MOEA approaches. This insight is necessary to increase the effectiveness of all MOEA approaches. In order to increase MOEA computational efficiency parallelization of MOEAs is addressed. Communications between processors in a parallel MOEA …
Data Mining Feature Subset Weighting And Selection Using Genetic Algorithms, Okan Yilmaz
Data Mining Feature Subset Weighting And Selection Using Genetic Algorithms, Okan Yilmaz
Theses and Dissertations
We present a simple genetic algorithm (sGA), which is developed under Genetic Rule and Classifier Construction Environment (GRaCCE) to solve feature subset selection and weighting problem to have better classification accuracy on k-nearest neighborhood (KNN) algorithm. Our hypotheses are that weighting the features will affect the performance of the KNN algorithm and will cause better classification accuracy rate than that of binary classification. The weighted-sGA algorithm uses real-value chromosomes to find the weights for features and binary-sGA uses integer-value chromosomes to select the subset of features from original feature set. A Repair algorithm is developed for weighted-sGA algorithm to guarantee …
Traveling Salesman Problem For Surveillance Mission Using Particle Swarm Optimization, Barry R. Secrest
Traveling Salesman Problem For Surveillance Mission Using Particle Swarm Optimization, Barry R. Secrest
Theses and Dissertations
The surveillance mission requires aircraft to fly from a starting point through defended terrain to targets and return to a safe destination (usually the starting point). The process of selecting such a flight path is known as the Mission Route Planning (MRP) Problem and is a three-dimensional, multi-criteria (fuel expenditure, time required, risk taken, priority targeting, goals met, etc.) path search. Planning aircraft routes involves an elaborate search through numerous possibilities, which can severely task the resources of the system being used to compute the routes. Operational systems can take up to a day to arrive at a solution due …
Implementation And Analysis Of The Parallel Genetic Rule And Classifier Construction Environment, David M. Strong
Implementation And Analysis Of The Parallel Genetic Rule And Classifier Construction Environment, David M. Strong
Theses and Dissertations
This paper discusses the Genetic Rule and Classifier Construction Environment (GRaCCE), which is an alternative to existing decision rule induction (DRI) algorithms. GRaCCE is a multi-phase algorithm which uses evolutionary search to mine classification rules from data. The current implementation uses a genetic algorithm based 0/1 search to reduce the number of features to a minimal set of features that make the most significant contributions to the classification of the input data set. This feature selection increases the efficiency of the rule induction algorithm that follows. However, feature selection is shown to account for more than 98 percent of the …
Protein Structure Prediction Using Parallel Linkage Investigating Genetic Algorithms, Karl R. Deerman
Protein Structure Prediction Using Parallel Linkage Investigating Genetic Algorithms, Karl R. Deerman
Theses and Dissertations
AFIT has had a long-standing interest in solving the protein structure prediction (PSP) problem. The PSP problem is an intractable problem that if "solved" can lead to revolutionary new techniques for everything from the development of new medicines to optical computer switches. The challenge is to find a reliable and consistent method of predicting the 3-dimensional structure of a protein given its defining sequence of amino acids. PSP is primarily concerned with predicting the tertiary protein structure without regards to how the protein came to this folded state. The tertiary structure determines the protein's functionality.
Refined Genetic Algorithms For Polypeptide Structure Prediction, Charles E. Kaiser Jr.
Refined Genetic Algorithms For Polypeptide Structure Prediction, Charles E. Kaiser Jr.
Theses and Dissertations
Accurate and reliable prediction of macromolecular structures has eluded researchers for nearly 40 years. Prediction via energy minimization assumes the native conformation has the globally minimal energy potential. An exhaustive search is impossible since for molecules of normal size, the size of the search space exceeds the size of the universe. Domain knowledge sources, such as the Brookhaven PDB can be mined for constraints to limit the search space. Genetic algorithms (GAs) are stochastic, population based, search algorithms of polynomial (P) time complexity that can produce semi-optimal solutions for problems of nondeterministic polynomial (NP) time complexity such as PSP. Three …
Analysis Of Linkage-Friendly Genetic Algorithms, Laurence D. Merkle
Analysis Of Linkage-Friendly Genetic Algorithms, Laurence D. Merkle
Theses and Dissertations
Evolutionary algorithms (EAs) are stochastic population-based algorithms inspired by the natural processes of selection, mutation, and recombination. EAs are often employed as optimum seeking techniques. A formal framework for EAs is proposed, in which evolutionary operators are viewed as mappings from parameter spaces to spaces of random functions. Formal definitions within this framework capture the distinguishing characteristics of the classes of recombination, mutation, and selection operators. EAs which use strictly invariant selection operators and order invariant representation schemes comprise the class of linkage-friendly genetic algorithms (lfGAs). Fast messy genetic algorithms (fmGAs) are lfGAs which use binary tournament selection (BTS) with …
The Application Of Hybridized Genetic Algorithms To The Protein Folding Problem, Robert L. Gaulke
The Application Of Hybridized Genetic Algorithms To The Protein Folding Problem, Robert L. Gaulke
Theses and Dissertations
The protein folding problem consists of attempting to determine the native conformation of a protein given its primary structure. This study examines various methods of hybridizing a genetic algorithm implementation in order to minimize an energy function and predict the conformation (structure) of Met-enkephalin. Genetic Algorithms are semi-optimal algorithms designed to explore and exploit a search space. The genetic algorithm uses selection, recombination, and mutation operators on populations of strings which represent possible solutions to the given problem. One step in solving the protein folding problem is the design of efficient energy minimization techniques. A conjugate gradient minimization technique is …
Predicting Protein Structure Using Parallel Genetic Algorithms, George H. Gates Jr.
Predicting Protein Structure Using Parallel Genetic Algorithms, George H. Gates Jr.
Theses and Dissertations
The protein folding problem is a biochemistry Grand Challenge problem. The challenge is to reliably predict natural three-dimensional structures of polypeptides. Genetic algorithms (GAs) are robust, semi-optimal search techniques modeling natural evolutionary processes. Fast messy GAs (fmGAs) are variants of messy GAs that reduce the exponential time complexity to polynomial. This investigation evaluates the merits of parallel SGAs and fmGAs for minimizing the potential energy of a pentapeptide, (Met)-enkephalin. AFIT's energy model is compared to a similar model in a commercial package called QUANTA. Differences between the two models are identified and resolved to enhance GAs' abilities to correctly fold …
Genetic Algorithms And Their Application To The Protein Folding Problem, Donald J. Brinkman
Genetic Algorithms And Their Application To The Protein Folding Problem, Donald J. Brinkman
Theses and Dissertations
The protein folding problem involves the prediction of the secondary and tertiary structure of a molecule given the primary structure. The primary structure defines sequence of amino-acid residues, while the secondary structure describes the local 3-dimensional arrangement of amino-acid residues within the molecule. The relative orientation of the secondary structural motifs, namely the tertiary structure, defines the shape of the entire biomolecule. The exact, mechanism by which a sequence of amino acids protein folds into its 3- dimensional conformation is unknown Current approaches to the protein folding problem include calculus-based methods, systematic search, model building and symbolic methods, random methods …
Discovery Learning In Autonomous Agents Using Genetic Algorithms, Edward O. Gordon
Discovery Learning In Autonomous Agents Using Genetic Algorithms, Edward O. Gordon
Theses and Dissertations
As the new Distributed Interactive Simulation (DIS) draft standard evolves into a useful document and distributed simulations begin to emerge that implement parts of the standard, there is renewed interest in available methods to effectively control autonomous aircraft agents in such a simulated environment. This investigation examines the use of a genetics-based classifier system for agent control. These are robust learning systems that use the adaptive search mechanisms of genetic algorithms to guide the learning system in forming new concepts (decision rules) about its environment. By allowing the rule base to evolve, it adapts agent behavior to environmental changes. Addressed …