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dc.contributor.advisorRoss, I. Michael
dc.contributor.authorMcGrath, Christopher B.
dc.dateSep-16
dc.date.accessioned2016-11-02T17:18:43Z
dc.date.available2016-11-02T17:18:43Z
dc.date.issued2016-09
dc.identifier.urihttp://hdl.handle.net/10945/50592
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractThis dissertation investigates several innovative approaches to evolutionary optimization that are relevant to numerous applications in astronautical engineering. The challenges and shortfalls associated with evolutionary algorithms are translated into three overarching goals that directly motivate the research and innovations of this dissertation. The first goal is to investigate and employ techniques that enable evolutionary algorithms to effectively handle constraints in a way that allows for feasible solutions to constrained optimization problems. The second goal is to improve computation times and efficiencies associated with evolutionary algorithms. The last goal is to enhance the evolutionary algorithm's robustness and ability to consistently find accurate solutions within a finite number of iterations. Novel techniques involving the application of unscented sampling, parallel computation, and various forms of exact penalty functions are developed and applied to both genetic algorithms and evolution strategies to achieve these goals. The results of this research offer a promising new set of modified evolutionary algorithms that outperform state-of-the-art techniques on a number of challenging multimodal optimization problems. In addition, these new methods are shown to be very effective in solving a minimum-propellant lunar lander optimal control problem, representing a class of problems that are historically difficult to solve using evolutionary algorithms.en_US
dc.description.urihttp://archive.org/details/unscentedsamplin1094550592
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleUnscented sampling techniques for evolutionary computation with applications to astrodynamic optimizationen_US
dc.typeThesisen_US
dc.contributor.departmentMechanical and Aerospace Engineering (MAE)
dc.subject.authorevolutionary algorithmen_US
dc.subject.authorevolution strategyen_US
dc.subject.authorgenetic algorithmen_US
dc.subject.authorparallel computationen_US
dc.subject.authorparallel processingen_US
dc.subject.authorun-scented samplingen_US
dc.description.serviceCaptain, United States Air Forceen_US
etd.thesisdegree.nameDoctor of Philosophy in Astronautical Engineeringen_US
etd.thesisdegree.levelDoctoralen_US
etd.thesisdegree.disciplineAstronautical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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