Show simple item record

dc.contributor.authorPrice, Collin
dc.date.accessioned2014-10-30T19:27:06Z
dc.date.available2014-10-30T19:27:06Z
dc.date.issued2014-10-30
dc.identifier.urihttp://hdl.handle.net/10464/5819
dc.description.abstractExperimental Extended X-ray Absorption Fine Structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, pre- dicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proven somewhat successful in structure refinement but have not been successful in finding the global minima. Multiple population based algorithms, including a genetic algorithm, a restarting ge- netic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure refinement of EXAFS. The oxygen-evolving com- plex in S1 is used as a benchmark for comparing the algorithms. These algorithms were successful in finding new atomic structures that produced improved calculated EXAFS spectra over atomic structures previously found.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectcomputational intelligence, metal protein complex, exafsen_US
dc.titleModeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using Computational Intelligenceen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
dc.embargo.termsNoneen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record