Genetic Algorithm Optimisation of Distributed Database Queries

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{gregory:1998:GAoddq,
  author =       "Michael Gregory",
  title =        "Genetic Algorithm Optimisation of Distributed Database
                 Queries",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "271--276",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, algorithm
                 performance,combinatorial optimisation, cost reduction,
                 distributed relational database query optimisation,
                 local search phase, multistart, premature convergence,
                 random search, real-time query optimisation, simulated
                 annealing, stochastic optimisation techniques, table
                 reduction, tailored crossover operator, tailored
                 mutation operator, tree query, tree-structured data
                 model, distributed databases, mathematical operators,
                 query processing, real-time systems, relational
                 databases, software performance evaluation, tree data
                 structures",
  ISBN =         "0-7803-4869-9",
  file =         "c047.pdf",
  DOI =          "doi:10.1109/ICEC.1998.699724",
  size =         "6 pages",
  abstract =     "Distributed relational database query optimisation is
                 a combinatorial optimisation problem. This paper
                 reports on an initial investigation into the potential
                 for a genetic algorithm (GA) to optimise distributed
                 queries. A genetic algorithm is developed and its
                 performance compared with alternative stochastic
                 optimisation techniques: random search, multistart, and
                 simulated annealing. The problem of fully reducing all
                 tables in a tree query is used to compare the
                 techniques. For this problem, evaluating the fitness
                 function is an expensive operation. The proposed GA
                 uses a tree-structured data model with tailored
                 crossover and mutation operators that avoid the need to
                 fully re-evaluate the fitness function for new
                 solutions. Query optimisation is a task that must be
                 performed in real-time. A technique is required that
                 performs well at the start of a search, but avoids the
                 problem of premature convergence. The proposed GA uses
                 a local search phase to deliver the required real-time
                 performance. Experiments show that the proposed GA can
                 perform better than the alternative techniques tested.
                 The potential for a GA to deliver valuable distributed
                 query processing cost reductions is demonstrated.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence. Also
                 known as \cite{699724}",
}

Genetic Programming entries for Michael Gregory

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