Searching for Search Algorithms: Experiments in Meta-search

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  title =        "Searching for Search Algorithms: Experiments in
  author =       "Brian J. Ross",
  year =         "2002",
  month =        dec # "~06",
  institution =  "Department of Computer Science, Brock University",
  type =         "Technical Report",
  number =       "CS-02-23",
  address =      "St. Catharines, Ontario, Canada L2S 3A1
  keywords =     "genetic algorithms, genetic programming, Meta-search,
  citeseer-isreferencedby = "oai:CiteSeerPSU:95028",
  citeseer-references = "oai:CiteSeerPSU:466766; oai:CiteSeerPSU:345471;
                 oai:CiteSeerPSU:513339; oai:CiteSeerPSU:75066;
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:561669",
  rights =       "unrestricted",
  URL =          "",
  URL =          "",
  abstract =     "The conventional approach to solving optimization and
                 search problems is to apply a variety of search
                 algorithms to the problem at hand, in order to discover
                 a technique that is well-adapted to the search space
                 being explored. This paper investigates an alternative
                 approach, in which search algorithms are automatically
                 synthesized for particular optimization problem
                 instances. A language composed of potentially useful
                 basic search primitives is derived. This search
                 language is then used with genetic programming to
                 derive search algorithms. The genetic programming
                 system evaluates the fitness of each search algorithm
                 by applying it to a binary-encoded optimization problem
                 (Traveling Salesman), and measuring the relative
                 performance of that algorithm in finding a solution to
                 the problem. It is shown that the evolved search
                 algorithms often display consistent characteristics
                 with respect to the corresponding problem instance to
                 which they are applied. For example, some problem
                 instances are best suited to hill-climbing, while
                 others are better adapted to conventional genetic
                 algorithms. As is to be expected, the search algorithm
                 derived is strongly dependent the scale and
                 representation of the problem explored, the amount of
                 computational e#ort allotted to the overall search, and
                 the search primitives available for the algorithm.
                 Additionally, some insights are gained into issues of
                 genetic algorithm search. A novel {"}memetic
                 crossover{"} operator was evolved during the course of
                 this research.",
  notes =        "Submitted for publication.",
  size =         "25 pages",

Genetic Programming entries for Brian J Ross