Genetic network parameter estimation using single and multi-objective particle swarm optimization

dc.contributor.authorMorcos, Karim M.
dc.date.accessioned2011-05-27T13:52:04Z
dc.date.available2011-05-27T13:52:04Z
dc.date.graduationmonthMay
dc.date.issued2011-05-27
dc.date.published2011
dc.description.abstractMulti-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.
dc.description.advisorSanjoy Das
dc.description.advisorStephen M. Welch
dc.description.degreeMaster of Science
dc.description.departmentDepartment of Electrical and Computer Engineering
dc.description.levelMasters
dc.identifier.urihttp://hdl.handle.net/2097/9207
dc.language.isoen_US
dc.publisherKansas State University
dc.rights© the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectParticle Swarm Optimization
dc.subjectGenetic network
dc.subjectMulti-objective optimization
dc.subjectArtificial Intelligence
dc.subjectEvolutionary algorithms
dc.subjectGenetic algorithms
dc.subject.umiArtificial Intelligence (0800)
dc.subject.umiBioinformatics (0715)
dc.subject.umiBiology, Plant Physiology (0817)
dc.subject.umiComputer Engineering (0464)
dc.subject.umiEngineering (0537)
dc.subject.umiInformation Science (0723)
dc.subject.umiPlant Pathology (0480)
dc.subject.umiPlant Sciences (0479)
dc.titleGenetic network parameter estimation using single and multi-objective particle swarm optimization
dc.typeThesis

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