Evolving comprehensible and scalable solvers using CGP for solving some real-world inspired problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Patricia Ryser-Welch",
  title =        "Evolving comprehensible and scalable solvers using
                 {CGP} for solving some real-world inspired problems",
  school =       "Electronic Engineering, University of York",
  year =         "2017",
  address =      "UK",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming",
  URL =          "https://www.researchgate.net/publication/321867825_Evolving_comprehensible_and_scalable_solvers_using_CGP_for_solving_some_real-world_inspired_problems",
  URL =          "https://www.researchgate.net/profile/Patricia_Ryser-Welch/publication/321867825_Evolving_comprehensible_and_scalable_solvers_using_CGP_for_solving_some_real-world_inspired_problems/links/5a366b1945851532e83025db/Evolving-comprehensible-and-scalable-solvers-using-CGP-for-solving-some-real-world-inspired-problems.pdf",
  size =         "372 pages",
  abstract =     "My original contribution to knowledge is the
                 application of Cartesian Genetic Programming to design
                 some scalable and human-understandable metaheuristics
                 automatically; those find some suitable solutions for
                 real-world NP-hard and discrete problems. This
                 technique is thought to possess the ability to raise
                 the generality of a problem-solving process, allowing
                 some supervised machine learning tasks and being able
                 to evolve non-deterministic algorithms.

                 Two extensions of Cartesian Genetic Programming are
                 presented. Iterative Cartesian Genetic Programming can
                 encode loops and nested loop with their termination
                 criteria, making susceptible to evolutionary
                 modification the whole programming construct. This
                 newly developed extension and its application to
                 metaheuristics are demonstrated to discover effective
                 solvers for NP-hard and discrete problems. This thesis
                 also extends Cartesian Genetic Programming and
                 Iterative Cartesian Genetic Programming to adapt a
                 hyper-heuristic reproductive operator at the same time
                 of exploring the automatic design space. It is
                 demonstrated the exploration of an automated design
                 space can be improved when specific types of active and
                 non-active genes are mutated.

                 A series of rigorous empirical investigations
                 demonstrate that lowering the comprehension barrier of
                 automatically designed algorithms can help
                 communicating and identifying an effective and
                 ineffective pattern of primitives. The complete
                 evolution of loops and nested loops without imposing a
                 hard limit on the number of recursive calls is shown to
                 broaden the automatic design space. Finally, it is
                 argued the capability of a learning objective function
                 to assess the scalable potential of a generated
                 algorithm can be beneficial to a generative
  notes =        "Supervisor Julian Miller",

Genetic Programming entries for Patricia Ryser-Welch