Regression and Classification from Extinction

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{Brown:thesis,
  author =       "Joseph Alexander Brown",
  title =        "Regression and Classification from Extinction",
  school =       "School of Computer Science, The University of Guelph",
  year =         "2014",
  address =      "Canada",
  month =        "10 " # jan,
  keywords =     "genetic algorithms, genetic programming
                 Bioinformatics",
  URL =          "http://hdl.handle.net/10214/7793",
  URL =          "https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7793",
  URL =          "https://atrium.lib.uoguelph.ca/xmlui/bitstream/handle/10214/7793/Brown_Joseph_201401_PhD.pdf",
  URL =          "http://genealogy.math.ndsu.nodak.edu/id.php?id=188278",
  size =         "176 pages",
  abstract =     "Evolutionary Algorithms use the principles of natural
                 selection and biological evolution to act as search and
                 optimisation tools. Two novel Spatially Structured
                 Evolutionary Algorithms: the Multiple Worlds Model
                 (MWM) and Multiple Agent Genetic Networks (MAGnet) are
                 presented. These evolutionary algorithms create evolved
                 unsupervised classifiers for data. Both have a property
                 of subpopulation collapse, where a population/node
                 receives little or no fitness implying the number of
                 classes is too large. This property has the best
                 biological analog of extinction.

                 MWM has a number of evolving populations of candidate
                 solutions. The novel fitness function selects one
                 member from each population, and fitness is divided
                 between. Each of these populations meets with the
                 biological definition of a separate species; each is a
                 group of organisms which produces offspring within
                 their type, but not outside of it. This fitness
                 function creates an unsupervised classification by
                 partitioning the data, based on which population is of
                 highest fitness, and creates an evolved classifier for
                 that partition.

                 MAGnet involves a number of evolving agents spread
                 about a graph, the nodes of which contain individual
                 data members or problem instances. The agents will in
                 turn test their fitness on each of the neighbouring
                 nodes in the graph, moving to the one where they have
                 the highest fitness. During this move they may choose
                 to take one of these problem instances with them. The
                 agent then undergoes evolutionary operations based on
                 which neighbours are on the node. The locations of the
                 problem instances over time are sorted by the evolving
                 agents, and the agents on a node act as a classifier",
  notes =        "Advisor: Ashlock, Daniel",
}

Genetic Programming entries for Joseph Alexander Brown

Citations