A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@Article{Hyde:2011:ieeeTEC,
  author =       "Edmund K. Burke and Matthew Hyde and 
                 Graham Kendall and John Woodward",
  title =        "A Genetic Programming Hyper-Heuristic Approach for
                 Evolving 2-{D} Strip Packing Heuristics",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2010",
  volume =       "14",
  number =       "6",
  pages =        "942--958",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, volutionary
                 computation, evolving 2D strip packing heuristics,
                 genetic programming hyper heuristic approach, search
                 methodologies, computational complexity, search
                 problems",
  ISSN =         "1089-778X",
  doi =          "doi:10.1109/TEVC.2010.2041061",
  abstract =     "We present a genetic programming (GP) system to evolve
                 reusable heuristics for the 2-D strip packing problem.
                 The evolved heuristics are constructive, and decide
                 both which piece to pack next and where to place that
                 piece, given the current partial solution. This paper
                 contributes to a growing research area that represents
                 a paradigm shift in search methodologies. Instead of
                 using evolutionary computation to search a space of
                 solutions, we employ it to search a space of heuristics
                 for the problem. A key motivation is to investigate
                 methods to automate the heuristic design process. It
                 has been stated in the literature that humans are very
                 good at identifying good building blocks for solution
                 methods. However, the task of intelligently searching
                 through all of the potential combinations of these
                 components is better suited to a computer. With such
                 tools at their disposal, heuristic designers are then
                 free to commit more of their time to the creative
                 process of determining good components, while the
                 computer takes on some of the design process by
                 intelligently combining these components. This paper
                 shows that a GP hyper-heuristic can be employed to
                 automatically generate human competitive heuristics in
                 a very-well studied problem domain.",
  notes =        "also known as \cite{5491153}",
}

Genetic Programming entries for Edmund Burke Matthew R Hyde Graham Kendall John R Woodward