Automated heuristic design

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

@InProceedings{Ochoa:2011:GECCOcomp,
  author =       "Gabriela Ochoa and Matthew Hyde and Edmund K. Burke",
  title =        "Automated heuristic design",
  booktitle =    "GECCO 2011 Tutorials",
  year =         "2011",
  editor =       "Darrell Whitley",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1321--1342",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002139",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This tutorial will discuss state-of-the-art techniques
                 for automating the design of heuristic search methods,
                 in order to remove or reduce the need for a human
                 expert in the process of designing an effective
                 algorithm to solve a search problem. Using machine
                 learning or meta-level search, several approaches have
                 been proposed in computer science, artificial
                 intelligence and operational research. The aim is to
                 develop methodologies which can adapt to different
                 environments without manually having to customise the
                 search, or its parameters, for each particular problem
                 domain. This can be seen as one of the drawbacks of
                 many current metaheuristic and evolutionary
                 implementations, which tend to have to be customised
                 for a particular class of problems or even specific
                 problem instances. We have identified two main types of
                 approaches to this challenge: heuristic selection, and
                 heuristic generation. In heuristic selection the idea
                 is to automatically combine fixed pre-existing simple
                 heuristics or neighbourhood structures to solve the
                 problem at hand; whereas in heuristic generation the
                 idea is to automatically create new heuristics (or
                 heuristic components) suited to a given problem or
                 class of problems. This latter approach is typically
                 achieved by combining, through the use of genetic
                 programming for example, components or building-blocks
                 of human designed heuristics. This tutorial will go
                 over the intellectual roots and origins of both
                 automated heuristic selection and generation, before
                 discussing work carried out to date in these two
                 directions and then focusing on some observations and
                 promising research directions.",
  notes =        "Also known as \cite{2002139} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Gabriela Ochoa Matthew R Hyde Edmund Burke

Citations