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

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Xinye Cai",
  title =        "A multi-objective {GP-PSO} hybrid algorithm for gene
                 regulatory network modeling",
  school =       "Department of Electrical and Computer Engineering,
                 Kansas State University",
  year =         "2009",
  address =      "Manhattan, Kansas, USA",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, multi-objective optimization,
                 particle swarm optimization, gene regulatory network
                 modelling, plant breeding simulation, Arabidopsis, NK
                 fitness landscape models",
  URL =          "http://hdl.handle.net/2097/1492",
  URL =          "http://krex.k-state.edu/dspace/handle/2097/1492",
  URL =          "http://krex.k-state.edu/dspace/bitstream/handle/2097/1492/xinyecai2009.pdf",
  size =         "130 pages",
  abstract =     "Stochastic algorithms are widely used in various
                 modelling 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 behaviour 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",
  notes =        "Supervisor Sanjoy Das",

Genetic Programming entries for Xinye Cai