A multi-objective GP-PSO hybrid algorithm for gene regulatory network modeling

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dc.contributor.author Cai, Xinye
dc.date.accessioned 2009-05-19T13:54:08Z
dc.date.available 2009-05-19T13:54:08Z
dc.date.issued 2009-05-19T13:54:08Z
dc.identifier.uri http://hdl.handle.net/2097/1492
dc.description.abstract Stochastic algorithms are widely used in various modeling and optimization problems. Evolutionary algorithms are one class of population-based stochastic approaches that are inspired from Darwinian evolutionary theory. A population of candidate solutions is initialized at the first generation of the algorithm. Two variation operators, crossover and mutation, that mimic the real world evolutionary process, are applied on the population to produce new solutions from old ones. Selection based on the concept of survival of the fittest is used to preserve parent solutions for next generation. Examples of such algorithms include genetic algorithm (GA) and genetic programming (GP). Nevertheless, other stochastic algorithms may be inspired from animals’ behavior such as particle swarm optimization (PSO), which imitates the cooperation of a flock of birds. In addition, stochastic algorithms are able to address multi-objective optimization problems by using the concept of dominance. Accordingly, a set of solutions that do not dominate each other will be obtained, instead of just one best solution. This thesis proposes a multi-objective GP-PSO hybrid algorithm to recover gene regulatory network models that take environmental data as stimulus input. The algorithm infers a model based on both phenotypic and gene expression data. The proposed approach is able to simultaneously infer network structures and estimate their associated parameters, instead of doing one or the other iteratively as other algorithms need to. In addition, a non-dominated sorting approach and an adaptive histogram method based on the hypergrid strategy are adopted to address ‘convergence’ and ‘diversity’ issues in multi-objective optimization. Gene network models obtained from the proposed algorithm are compared to a synthetic network, which mimics key features of Arabidopsis flowering control system, visually and numerically. Data predicted by the model are compared to synthetic data, to verify that they are able to closely approximate the available phenotypic and gene expression data. At the end of this thesis, a novel breeding strategy, termed network assisted selection, is proposed as an extension of our hybrid approach and application of obtained models for plant breeding. Breeding simulations based on network assisted selection are compared to one common breeding strategy, marker assisted selection. The results show that NAS is better both in terms of breeding speed and final phenotypic level. en
dc.description.sponsorship National Science Foundation en
dc.language.iso en_US en
dc.publisher Kansas State University en
dc.subject multi-objective optimization en
dc.subject genetic programming en
dc.subject particle swarm optimization en
dc.subject gene regulatory network modeling en
dc.subject plant breeding simulation en
dc.subject NK fitness landscape models en
dc.title A multi-objective GP-PSO hybrid algorithm for gene regulatory network modeling en
dc.type Dissertation en
dc.description.degree Doctor of Philosophy en
dc.description.level Doctoral en
dc.description.department Department of Electrical and Computer Engineering en
dc.description.advisor Sanjoy Das en
dc.subject.umi Artificial Intelligence (0800) en
dc.subject.umi Computer Science (0984) en
dc.subject.umi Information Science (0723) en
dc.date.published 2009 en
dc.date.graduationmonth May en

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