Evolving Hyper-Heuristics using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Achiya Elyasaf",
  title =        "Evolving Hyper-Heuristics using Genetic Programming",
  school =       "Ben-Gurion University of the Negev",
  year =         "2014",
  address =      "Beer-Sheva, Israel",
  month =        "10 " # oct,
  keywords =     "genetic algorithms, genetic programming, Rush Hour,
                 FreeCell, HH-Evolver, IDA*, mRNA",
  URL =          "http://achiya.elyasaf.net/wp-content/uploads/2015/07/Achiya-Elyasaf-Ph.D.-Thesis.pdf",
  size =         "128 pages",
  abstract =     "The application of computational intelligence
                 techniques within the vast domain of games has been
                 increasing at a breathtaking speed. Over the past few
                 years my research has produced a plethora of results in
                 games of different natures, evidencing the success and
                 efficiency of evolutionary algorithms in general|and
                 genetic programming in particular|at producing
                 top-notch, human-competitive game strategies.

                 Studying games may advance our knowledge both in
                 cognition and artificial intelligence, and, last but
                 not least, games possess a competitive angle that
                 coincides with our human nature, thus motivating

                 In this dissertation I explore the application of
                 genetic programming to the development of search
                 heuristics for difficult games. I apply GP to the
                 evolution of solvers for the Rush Hour puzzle and the
                 game of FreeCell, along the way demonstrating a general
                 method for evolving heuristics.

                 My study produced two Gold and one Bronze HUMIE Awards,
                 and an IEEE Outstanding Paper Award.

                 Genetic Programming (GP) is a sub-class of evolutionary
                 algorithms, in which a population of solutions to a
                 given problem, embodied as LISP expressions, is
                 improved over time by applying the principles of
                 Darwinian evolution. At each stage, or generation,
                 every solution's quality is measured and assigned a
                 numerical value, called fitness. During the course of
                 evolution, natural (or, in our case, artificial)
                 selection takes place, wherein individuals with high
                 fitness values are more likely to generate

                 Following selection, genetic operators are applied to
                 the selected individuals. The most widely used ones are
                 crossover, reproduction, and mutation. The crossover
                 (or recombination) operation is reminiscent of natural
                 gene transfer from parents to offspring.....",
  notes =        "supervisor: Moshe Sipper",

Genetic Programming entries for Achiya Elyasaf