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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 …
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 …
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 …
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 …
Generalization And Parallelization Of Messy Genetic Algorithms And Communication In Parallel Genetic Algorithms., Laurence D. Merkle
Generalization And Parallelization Of Messy Genetic Algorithms And Communication In Parallel Genetic Algorithms., Laurence D. Merkle
Theses and Dissertations
Genetic algorithms (GA) are highly parallelizable, robust semi- optimization algorithms of polynomial complexity. The most commonly implemented GAs are 'simple' GAs (SGAs). Reproduction, crossover, and mutation operate on solution populations. Deceptive and GA-hard problems are provably difficult for simple GAs. Messy GAs (MGA) are designed to overcome these limitations. The MGA is generalized to solve permutation type optimization problems. Its performance is compared to another MGA's, an SGA's, and a permutation SGA's. Against a fully deceptive problem the generalized MGA (GMGA) consistently performs better than the simple GA. Against an NP-complete permutation problem, the GMGA performs better than the other …