A Comparison of Genetic Programming Variants for Hyper-Heuristics

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

@InProceedings{Harris:2015:GECCOcomp,
  author =       "Sean Harris and Travis Bueter and Daniel R. Tauritz",
  title =        "A Comparison of Genetic Programming Variants for
                 Hyper-Heuristics",
  booktitle =    "GECCO 2015 5th Workshop on Evolutionary Computation
                 for the Automated Design of Algorithms (ECADA'15)",
  year =         "2015",
  editor =       "John Woodward and Daniel Tauritz and 
                 Manuel Lopez-Ibanez",
  isbn13 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1043--1050",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739482.2768456",
  DOI =          "doi:10.1145/2739482.2768456",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "General-purpose optimization algorithms are often not
                 well suited for real-world scenarios where many
                 instances of the same problem class need to be
                 repeatedly and efficiently solved. Hyper-heuristics
                 automate the design of algorithms for a particular
                 scenario, making them a good match for real-world
                 problem solving. For instance, hardware model checking
                 induced Boolean Satisfiability Problem (SAT) instances
                 have a very specific distribution which general SAT
                 solvers are not necessarily well targeted to.
                 Hyper-heuristics can automate the design of a SAT
                 solver customized to a specific distribution of SAT
                 instances.

                 The first step in employing a hyper-heuristic is
                 creating a set of algorithmic primitives appropriate
                 for tackling a specific problem class. The second step
                 is searching the associated algorithmic primitive
                 space. Hyper-heuristics have typically employed Genetic
                 Programming (GP) to execute the second step, but even
                 in GP there are many alternatives. This paper reports
                 on an investigation of the relationship between the
                 choice of GP type and the performance obtained by a
                 hyper-heuristic employing it. Results are presented on
                 SAT, demonstrating the existence of problems for which
                 there is a statistically significant performance
                 differential between the use of different GP types.",
  notes =        "Also known as \cite{2768456} Distributed at
                 GECCO-2015.",
}

Genetic Programming entries for Sean Harris Travis Bueter Daniel R Tauritz

Citations