New Algorithms for Evolving Robust Genetic Programming Solutions in Dynamic Environments with a Real World Case Study in Hedge Fund Stock Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{WeiYan:thesis,
  author =       "Wei Yan",
  title =        "New Algorithms for Evolving Robust Genetic Programming
                 Solutions in Dynamic Environments with a Real World
                 Case Study in Hedge Fund Stock Selection",
  year =         "2012",
  school =       "Computer Science, University College, London",
  address =      "UK",
  month =        dec # "~28",
  keywords =     "genetic algorithms, genetic programming",
  oai =          "oai:eprints.ucl.ac.uk.OAI2:1380128",
  bibsource =    "OAI-PMH server at discovery.ucl.ac.uk",
  language =     "eng",
  oai =          "oai:eprints.ucl.ac.uk.OAI2:1380128",
  URL =          "http://discovery.ucl.ac.uk/1380128/",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=59&uin=uk.bl.ethos.625961",
  size =         "123 pages",
  abstract =     "This thesis presents three new genetic programming
                 (GP) algorithms designed to enhance robustness of
                 solutions evolved in highly dynamic environments and
                 investigates the application of the new algorithms to
                 financial time series analysis. The research is
                 motivated by the following thesis question: what are
                 viable strategies to enhance the robustness of GP
                 individuals when the environment of a task being
                 optimised or learnt by a GP system is characterised by
                 large, rapid, frequent and low-predictability changes?
                 The vast majority of existing techniques aim to track
                 dynamics of optima in very simple dynamic environments.
                 But the research area in improving robustness in
                 dynamic environments characterised by large, frequent
                 and unpredictable changes is not yet widely explored.
                 The three new algorithms were designed specifically to
                 evolve robust solutions in these environments. The
                 first algorithm {`}behavioural diversity
                 preservation{'} is a novel diversity preservation
                 technique. The algorithm evolves more robust solutions
                 by preserving population phenotypic diversity through
                 the reduction of their behavioural inter-correlation
                 and the promotion of individuals with unique behaviour.
                 The second algorithm {`}multiple-scenario training{'}
                 is a novel population training and evaluation
                 technique. The algorithm evolves more robust solutions
                 by training a population simultaneously across a set of
                 pre-constructed environment scenarios and by using a
                 {`}consistency-adjusted{'} fitness measure to favour
                 individuals performing well across the entire range of
                 environment scenarios. The third algorithm {`}committee
                 voting{'} is a novel {`}final solution{'} selection
                 technique. The algorithm enhances robustness by
                 breaking away from {`}best-of-run{'} tradition,
                 creating a solution based on a majority-voting
                 committee structure consisting of individuals evolved
                 in a range of diverse environmental dynamics. The
                 thesis introduces a comprehensive real-world case
                 application for the evaluation experiments. The case is
                 a hedge fund stock selection application for a typical
                 long-short market neutral equity strategy in the
                 Malaysian stock market. The underlying technology of
                 the stock selection system is GP which assists to
                 select stocks by exploiting the underlying nonlinear
                 relationship between diverse ranges of influencing
                 factors. The three proposed algorithms are all applied
                 to this case study during evaluation. The results of
                 experiments based on the case study demonstrate that
                 all three new algorithms overwhelmingly outperform
                 canonical GP in two aspects of the robustness criteria
                 and conclude they are viable strategies for improving
                 robustness of GP individuals when the environment of a
                 task being optimised or learnt by a GP system is
                 characterised by large, sudden, frequent and
                 unpredictable changes.",
  notes =        "Supervisor: Christopher D. Clack Full text not
                 available from this repository. uk.bl.ethos.625961

                 UCL internal:001653187",
}

Genetic Programming entries for Wei Yan

Citations